Overview

Brought to you by YData

Dataset statistics

Number of variables133
Number of observations23654
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory105.7 MiB
Average record size in memory4.6 KiB

Variable types

Numeric17
Categorical116

Alerts

ALC_010 is highly overall correlated with CI_010 and 4 other fieldsHigh correlation
ALC_080 is highly overall correlated with CAN_050 and 4 other fieldsHigh correlation
BEH_010 is highly overall correlated with BEH_030 and 1 other fieldsHigh correlation
BEH_020 is highly overall correlated with BEH_040 and 1 other fieldsHigh correlation
BEH_030 is highly overall correlated with BEH_010 and 2 other fieldsHigh correlation
BEH_040 is highly overall correlated with BEH_020 and 2 other fieldsHigh correlation
BS_010 is highly overall correlated with BZP_010 and 5 other fieldsHigh correlation
BUL_010 is highly overall correlated with SEQIDHigh correlation
BUL_020 is highly overall correlated with BUL_030 and 2 other fieldsHigh correlation
BUL_030 is highly overall correlated with BUL_020 and 2 other fieldsHigh correlation
BUL_040 is highly overall correlated with SEQIDHigh correlation
BUL_050 is highly overall correlated with SEQIDHigh correlation
BUL_060 is highly overall correlated with BUL_020 and 2 other fieldsHigh correlation
BUL_070 is highly overall correlated with SEQIDHigh correlation
BUL_080 is highly overall correlated with BUL_120 and 1 other fieldsHigh correlation
BUL_090 is highly overall correlated with BUL_120 and 1 other fieldsHigh correlation
BUL_100 is highly overall correlated with SEQIDHigh correlation
BUL_110 is highly overall correlated with SEQIDHigh correlation
BUL_120 is highly overall correlated with BUL_080 and 2 other fieldsHigh correlation
BZP_010 is highly overall correlated with BS_010 and 6 other fieldsHigh correlation
CAN_010 is highly overall correlated with CAN_130 and 14 other fieldsHigh correlation
CAN_050 is highly overall correlated with ALC_080 and 4 other fieldsHigh correlation
CAN_130 is highly overall correlated with CAN_010 and 2 other fieldsHigh correlation
CAN_140 is highly overall correlated with CAN_010 and 8 other fieldsHigh correlation
CA_020 is highly overall correlated with ALC_080 and 4 other fieldsHigh correlation
CI_010 is highly overall correlated with ALC_010 and 9 other fieldsHigh correlation
COC_010 is highly overall correlated with POLY_050 and 1 other fieldsHigh correlation
COC_030 is highly overall correlated with HAL_030 and 4 other fieldsHigh correlation
DEX_010 is highly overall correlated with SEQIDHigh correlation
DR_010 is highly overall correlated with SEQIDHigh correlation
DR_020 is highly overall correlated with SEQIDHigh correlation
DR_060 is highly overall correlated with SEQIDHigh correlation
DR_070 is highly overall correlated with SEQIDHigh correlation
DVGENDER is highly overall correlated with SEQIDHigh correlation
DVLAST30 is highly overall correlated with DVTY1ST and 3 other fieldsHigh correlation
DVORIENT is highly overall correlated with SEQIDHigh correlation
DVRES is highly overall correlated with SEQIDHigh correlation
DVTY1ST is highly overall correlated with DVLAST30 and 2 other fieldsHigh correlation
DVTY2ST is highly overall correlated with CAN_010 and 10 other fieldsHigh correlation
DVURBAN is highly overall correlated with SEQIDHigh correlation
ELC_026a is highly overall correlated with ALC_010 and 6 other fieldsHigh correlation
ELC_026b is highly overall correlated with CAN_010 and 1 other fieldsHigh correlation
ELC_026c is highly overall correlated with SEQIDHigh correlation
ELC_041 is highly overall correlated with ALC_080 and 4 other fieldsHigh correlation
ELC_042 is highly overall correlated with ALC_080 and 4 other fieldsHigh correlation
GLU_010 is highly overall correlated with SEQIDHigh correlation
GRV_010 is highly overall correlated with SEQIDHigh correlation
HAL_010 is highly overall correlated with SEQIDHigh correlation
HAL_030 is highly overall correlated with COC_030 and 4 other fieldsHigh correlation
HER_010 is highly overall correlated with BS_010 and 7 other fieldsHigh correlation
MET_010 is highly overall correlated with BZP_010 and 5 other fieldsHigh correlation
MET_030 is highly overall correlated with COC_030 and 4 other fieldsHigh correlation
NRG_010 is highly overall correlated with SEQIDHigh correlation
NRG_020 is highly overall correlated with SEQIDHigh correlation
NRG_030 is highly overall correlated with SEQIDHigh correlation
NRG_040 is highly overall correlated with SEQIDHigh correlation
NRG_050 is highly overall correlated with ELC_026a and 1 other fieldsHigh correlation
PH_010 is highly overall correlated with PH_020 and 7 other fieldsHigh correlation
PH_020 is highly overall correlated with PH_010 and 6 other fieldsHigh correlation
PH_030 is highly overall correlated with PH_010 and 3 other fieldsHigh correlation
PH_040 is highly overall correlated with PH_020 and 6 other fieldsHigh correlation
PH_051 is highly overall correlated with PH_010 and 8 other fieldsHigh correlation
PH_052 is highly overall correlated with PH_010 and 4 other fieldsHigh correlation
PH_061 is highly overall correlated with PH_020 and 8 other fieldsHigh correlation
PH_062 is highly overall correlated with PH_051 and 3 other fieldsHigh correlation
PH_070 is highly overall correlated with PH_010 and 7 other fieldsHigh correlation
PH_080 is highly overall correlated with PH_020 and 7 other fieldsHigh correlation
PH_090 is highly overall correlated with PH_100 and 1 other fieldsHigh correlation
PH_100 is highly overall correlated with PH_020 and 4 other fieldsHigh correlation
PH_110 is highly overall correlated with PH_010 and 4 other fieldsHigh correlation
PH_120 is highly overall correlated with PH_010 and 5 other fieldsHigh correlation
PH_130 is highly overall correlated with PH_051 and 4 other fieldsHigh correlation
PH_140 is highly overall correlated with PH_040 and 5 other fieldsHigh correlation
POLY_010 is highly overall correlated with MET_010 and 7 other fieldsHigh correlation
POLY_020 is highly overall correlated with POLY_010 and 6 other fieldsHigh correlation
POLY_030 is highly overall correlated with SEQIDHigh correlation
POLY_040 is highly overall correlated with HER_010 and 7 other fieldsHigh correlation
POLY_050 is highly overall correlated with COC_010 and 5 other fieldsHigh correlation
POLY_060 is highly overall correlated with POLY_160 and 1 other fieldsHigh correlation
POLY_070 is highly overall correlated with POLY_020 and 5 other fieldsHigh correlation
POLY_080 is highly overall correlated with POLY_070 and 1 other fieldsHigh correlation
POLY_090 is highly overall correlated with POLY_100 and 1 other fieldsHigh correlation
POLY_100 is highly overall correlated with POLY_040 and 4 other fieldsHigh correlation
POLY_110 is highly overall correlated with SEQIDHigh correlation
POLY_120 is highly overall correlated with SEQIDHigh correlation
POLY_130 is highly overall correlated with POLY_010 and 6 other fieldsHigh correlation
POLY_140 is highly overall correlated with POLY_010 and 7 other fieldsHigh correlation
POLY_150 is highly overall correlated with POLY_010 and 6 other fieldsHigh correlation
POLY_160 is highly overall correlated with POLY_060 and 3 other fieldsHigh correlation
POLY_170 is highly overall correlated with POLY_100 and 6 other fieldsHigh correlation
POLY_180 is highly overall correlated with POLY_070 and 5 other fieldsHigh correlation
PROVID is highly overall correlated with WTPUMFHigh correlation
PR_030 is highly overall correlated with SEQIDHigh correlation
PR_050 is highly overall correlated with SEQIDHigh correlation
PR_060 is highly overall correlated with SEQIDHigh correlation
PR_090 is highly overall correlated with COC_030 and 5 other fieldsHigh correlation
PR_100 is highly overall correlated with SEQIDHigh correlation
SAL_010 is highly overall correlated with BS_010 and 5 other fieldsHigh correlation
SED_030 is highly overall correlated with SEQIDHigh correlation
SED_050 is highly overall correlated with SEQIDHigh correlation
SEQID is highly overall correlated with ALC_010 and 115 other fieldsHigh correlation
SLP_010 is highly overall correlated with SEQIDHigh correlation
SS_010 is highly overall correlated with CAN_010 and 15 other fieldsHigh correlation
STI_030 is highly overall correlated with SEQIDHigh correlation
STI_050 is highly overall correlated with SEQIDHigh correlation
STI_070 is highly overall correlated with PR_090 and 1 other fieldsHigh correlation
STI_080 is highly overall correlated with SEQIDHigh correlation
SYN_010 is highly overall correlated with SEQIDHigh correlation
TNB_010 is highly overall correlated with BS_010 and 6 other fieldsHigh correlation
TP_016 is highly overall correlated with SEQID and 1 other fieldsHigh correlation
TP_046 is highly overall correlated with SEQIDHigh correlation
TP_056 is highly overall correlated with SEQIDHigh correlation
TP_066 is highly overall correlated with SEQIDHigh correlation
TP_086 is highly overall correlated with SEQIDHigh correlation
TRP_010 is highly overall correlated with BS_010 and 6 other fieldsHigh correlation
TS_011 is highly overall correlated with DVLAST30 and 2 other fieldsHigh correlation
UND_010 is highly overall correlated with SEQID and 1 other fieldsHigh correlation
UND_020 is highly overall correlated with SEQID and 1 other fieldsHigh correlation
VAP_010 is highly overall correlated with ALC_010 and 5 other fieldsHigh correlation
VAP_020 is highly overall correlated with CAN_010 and 9 other fieldsHigh correlation
VAP_030 is highly overall correlated with ALC_010 and 9 other fieldsHigh correlation
VAP_040 is highly overall correlated with CAN_010 and 11 other fieldsHigh correlation
VAP_050a is highly overall correlated with CAN_010 and 9 other fieldsHigh correlation
VAP_050b is highly overall correlated with CAN_010 and 9 other fieldsHigh correlation
VAP_060 is highly overall correlated with CAN_010 and 5 other fieldsHigh correlation
WTPUMF is highly overall correlated with PROVIDHigh correlation
XTC_010 is highly overall correlated with POLY_020 and 1 other fieldsHigh correlation
XTC_030 is highly overall correlated with COC_030 and 4 other fieldsHigh correlation
DVORIENT is highly imbalanced (50.7%) Imbalance
TP_016 is highly imbalanced (84.3%) Imbalance
TP_046 is highly imbalanced (90.5%) Imbalance
TP_056 is highly imbalanced (76.1%) Imbalance
TP_066 is highly imbalanced (90.6%) Imbalance
TP_086 is highly imbalanced (93.4%) Imbalance
ELC_026b is highly imbalanced (68.0%) Imbalance
ELC_026c is highly imbalanced (71.3%) Imbalance
NRG_020 is highly imbalanced (50.6%) Imbalance
NRG_030 is highly imbalanced (68.0%) Imbalance
NRG_040 is highly imbalanced (69.1%) Imbalance
CAN_130 is highly imbalanced (64.4%) Imbalance
UND_010 is highly imbalanced (87.9%) Imbalance
UND_020 is highly imbalanced (89.3%) Imbalance
MET_010 is highly imbalanced (95.8%) Imbalance
XTC_010 is highly imbalanced (93.6%) Imbalance
HAL_010 is highly imbalanced (84.6%) Imbalance
HER_010 is highly imbalanced (97.3%) Imbalance
COC_010 is highly imbalanced (93.2%) Imbalance
SYN_010 is highly imbalanced (87.0%) Imbalance
BZP_010 is highly imbalanced (97.7%) Imbalance
BS_010 is highly imbalanced (96.3%) Imbalance
TNB_010 is highly imbalanced (97.9%) Imbalance
TRP_010 is highly imbalanced (97.0%) Imbalance
GLU_010 is highly imbalanced (91.9%) Imbalance
SAL_010 is highly imbalanced (96.8%) Imbalance
SLP_010 is highly imbalanced (78.8%) Imbalance
STI_030 is highly imbalanced (88.9%) Imbalance
DEX_010 is highly imbalanced (76.3%) Imbalance
GRV_010 is highly imbalanced (79.4%) Imbalance
STI_080 is highly imbalanced (61.6%) Imbalance
STI_050 is highly imbalanced (87.1%) Imbalance
SED_050 is highly imbalanced (72.6%) Imbalance
SED_030 is highly imbalanced (91.9%) Imbalance
PR_100 is highly imbalanced (65.9%) Imbalance
PR_030 is highly imbalanced (95.1%) Imbalance
PR_050 is highly imbalanced (96.7%) Imbalance
PR_060 is highly imbalanced (89.9%) Imbalance
POLY_010 is highly imbalanced (97.8%) Imbalance
POLY_020 is highly imbalanced (97.1%) Imbalance
POLY_030 is highly imbalanced (93.7%) Imbalance
POLY_040 is highly imbalanced (98.6%) Imbalance
POLY_050 is highly imbalanced (96.5%) Imbalance
POLY_060 is highly imbalanced (94.5%) Imbalance
POLY_070 is highly imbalanced (96.7%) Imbalance
POLY_080 is highly imbalanced (95.8%) Imbalance
POLY_090 is highly imbalanced (94.2%) Imbalance
POLY_100 is highly imbalanced (96.3%) Imbalance
POLY_110 is highly imbalanced (94.6%) Imbalance
POLY_120 is highly imbalanced (92.8%) Imbalance
POLY_130 is highly imbalanced (98.1%) Imbalance
POLY_140 is highly imbalanced (97.7%) Imbalance
POLY_150 is highly imbalanced (97.5%) Imbalance
POLY_160 is highly imbalanced (96.4%) Imbalance
POLY_170 is highly imbalanced (97.2%) Imbalance
POLY_180 is highly imbalanced (97.4%) Imbalance
DR_010 is highly imbalanced (82.5%) Imbalance
DR_020 is highly imbalanced (85.4%) Imbalance
BEH_010 is highly imbalanced (55.1%) Imbalance
BEH_020 is highly imbalanced (60.5%) Imbalance
BEH_030 is highly imbalanced (52.4%) Imbalance
BEH_040 is highly imbalanced (62.0%) Imbalance
BUL_010 is highly imbalanced (74.7%) Imbalance
BUL_040 is highly imbalanced (55.5%) Imbalance
BUL_050 is highly imbalanced (61.9%) Imbalance
BUL_060 is highly imbalanced (52.1%) Imbalance
BUL_070 is highly imbalanced (84.1%) Imbalance
BUL_080 is highly imbalanced (60.4%) Imbalance
BUL_090 is highly imbalanced (52.6%) Imbalance
BUL_100 is highly imbalanced (81.6%) Imbalance
BUL_110 is highly imbalanced (90.4%) Imbalance
BUL_120 is highly imbalanced (73.9%) Imbalance
DVTY1ST is highly imbalanced (92.6%) Imbalance
DVLAST30 is highly imbalanced (82.3%) Imbalance
SEQID has unique values Unique
WTPUMF has unique values Unique

Reproduction

Analysis started2025-04-17 05:05:06.289139
Analysis finished2025-04-17 05:07:09.217578
Duration2 minutes and 2.93 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

SEQID
Real number (ℝ)

High correlation  Unique 

Distinct23654
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30604.057
Minimum2
Maximum61096
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size369.6 KiB
2025-04-17T05:07:09.309335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3123.25
Q115309.25
median30575
Q345903.75
95-th percentile58086.35
Maximum61096
Range61094
Interquartile range (IQR)30594.5

Descriptive statistics

Standard deviation17645.274
Coefficient of variation (CV)0.5765665
Kurtosis-1.1994746
Mean30604.057
Median Absolute Deviation (MAD)15296.5
Skewness0.00030804015
Sum7.2390837 × 108
Variance3.113557 × 108
MonotonicityNot monotonic
2025-04-17T05:07:09.414616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20377 1
 
< 0.1%
20587 1
 
< 0.1%
5254 1
 
< 0.1%
32243 1
 
< 0.1%
37146 1
 
< 0.1%
21979 1
 
< 0.1%
59910 1
 
< 0.1%
54666 1
 
< 0.1%
22109 1
 
< 0.1%
28910 1
 
< 0.1%
Other values (23644) 23644
> 99.9%
ValueCountFrequency (%)
2 1
< 0.1%
3 1
< 0.1%
15 1
< 0.1%
18 1
< 0.1%
22 1
< 0.1%
23 1
< 0.1%
27 1
< 0.1%
28 1
< 0.1%
29 1
< 0.1%
30 1
< 0.1%
ValueCountFrequency (%)
61096 1
< 0.1%
61088 1
< 0.1%
61087 1
< 0.1%
61086 1
< 0.1%
61084 1
< 0.1%
61076 1
< 0.1%
61074 1
< 0.1%
61073 1
< 0.1%
61072 1
< 0.1%
61065 1
< 0.1%

PROVID
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.789676
Minimum10
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size369.6 KiB
2025-04-17T05:07:09.490535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q112
median35
Q348
95-th percentile59
Maximum59
Range49
Interquartile range (IQR)36

Descriptive statistics

Standard deviation17.310288
Coefficient of variation (CV)0.54452546
Kurtosis-1.4954692
Mean31.789676
Median Absolute Deviation (MAD)13
Skewness0.060580536
Sum751953
Variance299.64608
MonotonicityNot monotonic
2025-04-17T05:07:09.559710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
24 4197
17.7%
48 3904
16.5%
10 2879
12.2%
12 2648
11.2%
59 2425
10.3%
47 2287
9.7%
35 2266
9.6%
11 1909
8.1%
46 1139
 
4.8%
ValueCountFrequency (%)
10 2879
12.2%
11 1909
8.1%
12 2648
11.2%
24 4197
17.7%
35 2266
9.6%
46 1139
 
4.8%
47 2287
9.7%
48 3904
16.5%
59 2425
10.3%
ValueCountFrequency (%)
59 2425
10.3%
48 3904
16.5%
47 2287
9.7%
46 1139
 
4.8%
35 2266
9.6%
24 4197
17.7%
12 2648
11.2%
11 1909
8.1%
10 2879
12.2%

GRADE
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3567261
Minimum7
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size369.6 KiB
2025-04-17T05:07:09.621776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7
Q18
median9
Q311
95-th percentile12
Maximum12
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6285506
Coefficient of variation (CV)0.17405133
Kurtosis-1.1702576
Mean9.3567261
Median Absolute Deviation (MAD)1
Skewness0.090764008
Sum221324
Variance2.6521771
MonotonicityNot monotonic
2025-04-17T05:07:09.679663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 4434
18.7%
9 4347
18.4%
10 4163
17.6%
11 3956
16.7%
7 3893
16.5%
12 2861
12.1%
ValueCountFrequency (%)
7 3893
16.5%
8 4434
18.7%
9 4347
18.4%
10 4163
17.6%
11 3956
16.7%
12 2861
12.1%
ValueCountFrequency (%)
12 2861
12.1%
11 3956
16.7%
10 4163
17.6%
9 4347
18.4%
8 4434
18.7%
7 3893
16.5%

DVGENDER
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
12464 
1
11190 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 12464
52.7%
1 11190
47.3%

Length

2025-04-17T05:07:09.756066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:09.803106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 12464
52.7%
1 11190
47.3%

Most occurring characters

ValueCountFrequency (%)
2 12464
52.7%
1 11190
47.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 12464
52.7%
1 11190
47.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 12464
52.7%
1 11190
47.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 12464
52.7%
1 11190
47.3%

DVURBAN
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
19418 
2
4236 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 19418
82.1%
2 4236
 
17.9%

Length

2025-04-17T05:07:09.858082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:09.899816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 19418
82.1%
2 4236
 
17.9%

Most occurring characters

ValueCountFrequency (%)
1 19418
82.1%
2 4236
 
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 19418
82.1%
2 4236
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 19418
82.1%
2 4236
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 19418
82.1%
2 4236
 
17.9%

DVRES
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
18353 
2
2660 
3
2641 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 18353
77.6%
2 2660
 
11.2%
3 2641
 
11.2%

Length

2025-04-17T05:07:09.956913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:10.005535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 18353
77.6%
2 2660
 
11.2%
3 2641
 
11.2%

Most occurring characters

ValueCountFrequency (%)
1 18353
77.6%
2 2660
 
11.2%
3 2641
 
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 18353
77.6%
2 2660
 
11.2%
3 2641
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 18353
77.6%
2 2660
 
11.2%
3 2641
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 18353
77.6%
2 2660
 
11.2%
3 2641
 
11.2%

DVORIENT
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
21107 
1
2547 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 21107
89.2%
1 2547
 
10.8%

Length

2025-04-17T05:07:10.096656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:10.170060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 21107
89.2%
1 2547
 
10.8%

Most occurring characters

ValueCountFrequency (%)
2 21107
89.2%
1 2547
 
10.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 21107
89.2%
1 2547
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 21107
89.2%
1 2547
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 21107
89.2%
1 2547
 
10.8%

DVDESCRIBE
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2782193
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size369.6 KiB
2025-04-17T05:07:10.234112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2888859
Coefficient of variation (CV)1.004682
Kurtosis1.0447879
Mean2.2782193
Median Absolute Deviation (MAD)0
Skewness1.5871442
Sum53889
Variance5.2389988
MonotonicityNot monotonic
2025-04-17T05:07:10.323746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 16745
70.8%
8 2045
 
8.6%
5 1483
 
6.3%
4 1111
 
4.7%
3 794
 
3.4%
2 700
 
3.0%
7 487
 
2.1%
6 289
 
1.2%
ValueCountFrequency (%)
1 16745
70.8%
2 700
 
3.0%
3 794
 
3.4%
4 1111
 
4.7%
5 1483
 
6.3%
6 289
 
1.2%
7 487
 
2.1%
8 2045
 
8.6%
ValueCountFrequency (%)
8 2045
 
8.6%
7 487
 
2.1%
6 289
 
1.2%
5 1483
 
6.3%
4 1111
 
4.7%
3 794
 
3.4%
2 700
 
3.0%
1 16745
70.8%

WTPUMF
Real number (ℝ)

High correlation  Unique 

Distinct23654
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.365283
Minimum0.50738852
Maximum1561.2602
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size369.6 KiB
2025-04-17T05:07:10.438706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.50738852
5-th percentile1.9087022
Q15.7038334
median24.141672
Q338.193442
95-th percentile117.649
Maximum1561.2602
Range1560.7528
Interquartile range (IQR)32.489608

Descriptive statistics

Standard deviation59.360892
Coefficient of variation (CV)1.6785075
Kurtosis84.011925
Mean35.365283
Median Absolute Deviation (MAD)16.301822
Skewness7.0446114
Sum836530.41
Variance3523.7155
MonotonicityNot monotonic
2025-04-17T05:07:10.577554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87.86832969 1
 
< 0.1%
1.448577701 1
 
< 0.1%
10.07150425 1
 
< 0.1%
47.16534394 1
 
< 0.1%
24.62732077 1
 
< 0.1%
65.69606834 1
 
< 0.1%
95.57215414 1
 
< 0.1%
29.80193818 1
 
< 0.1%
24.46000984 1
 
< 0.1%
6.500755803 1
 
< 0.1%
Other values (23644) 23644
> 99.9%
ValueCountFrequency (%)
0.5073885226 1
< 0.1%
0.6115638838 1
< 0.1%
0.6168113391 1
< 0.1%
0.6698996294 1
< 0.1%
0.7179511146 1
< 0.1%
0.7485171814 1
< 0.1%
0.759562136 1
< 0.1%
0.7688730319 1
< 0.1%
0.780528451 1
< 0.1%
0.7817029455 1
< 0.1%
ValueCountFrequency (%)
1561.260211 1
< 0.1%
1262.507707 1
< 0.1%
1132.915615 1
< 0.1%
1126.11876 1
< 0.1%
992.2194307 1
< 0.1%
986.7209965 1
< 0.1%
977.558855 1
< 0.1%
958.9661181 1
< 0.1%
955.9665997 1
< 0.1%
937.9086347 1
< 0.1%

GH_010
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2906062
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size369.6 KiB
2025-04-17T05:07:10.681590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0611931
Coefficient of variation (CV)0.46328046
Kurtosis0.85285096
Mean2.2906062
Median Absolute Deviation (MAD)1
Skewness0.81741556
Sum54182
Variance1.1261308
MonotonicityNot monotonic
2025-04-17T05:07:10.765008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 8800
37.2%
3 6262
26.5%
1 5850
24.7%
4 2096
 
8.9%
6 332
 
1.4%
5 314
 
1.3%
ValueCountFrequency (%)
1 5850
24.7%
2 8800
37.2%
3 6262
26.5%
4 2096
 
8.9%
5 314
 
1.3%
6 332
 
1.4%
ValueCountFrequency (%)
6 332
 
1.4%
5 314
 
1.3%
4 2096
 
8.9%
3 6262
26.5%
2 8800
37.2%
1 5850
24.7%

GH_020
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9217469
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size369.6 KiB
2025-04-17T05:07:10.838838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3169725
Coefficient of variation (CV)0.45074832
Kurtosis-0.71177897
Mean2.9217469
Median Absolute Deviation (MAD)1
Skewness0.28638006
Sum69111
Variance1.7344165
MonotonicityNot monotonic
2025-04-17T05:07:10.938354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 6149
26.0%
2 6029
25.5%
4 4573
19.3%
1 3698
15.6%
5 2614
11.1%
6 591
 
2.5%
ValueCountFrequency (%)
1 3698
15.6%
2 6029
25.5%
3 6149
26.0%
4 4573
19.3%
5 2614
11.1%
6 591
 
2.5%
ValueCountFrequency (%)
6 591
 
2.5%
5 2614
11.1%
4 4573
19.3%
3 6149
26.0%
2 6029
25.5%
1 3698
15.6%

SS_010
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
20706 
1
2948 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 20706
87.5%
1 2948
 
12.5%

Length

2025-04-17T05:07:11.039929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:11.114326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 20706
87.5%
1 2948
 
12.5%

Most occurring characters

ValueCountFrequency (%)
2 20706
87.5%
1 2948
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 20706
87.5%
1 2948
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 20706
87.5%
1 2948
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 20706
87.5%
1 2948
 
12.5%

TS_011
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
18347 
3
3952 
2
 
864
1
 
491

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row3
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 18347
77.6%
3 3952
 
16.7%
2 864
 
3.7%
1 491
 
2.1%

Length

2025-04-17T05:07:11.199781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:11.274445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 18347
77.6%
3 3952
 
16.7%
2 864
 
3.7%
1 491
 
2.1%

Most occurring characters

ValueCountFrequency (%)
4 18347
77.6%
3 3952
 
16.7%
2 864
 
3.7%
1 491
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 18347
77.6%
3 3952
 
16.7%
2 864
 
3.7%
1 491
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 18347
77.6%
3 3952
 
16.7%
2 864
 
3.7%
1 491
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 18347
77.6%
3 3952
 
16.7%
2 864
 
3.7%
1 491
 
2.1%

TP_016
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
5
22337 
4
 
1033
3
 
215
2
 
42
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row4
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 22337
94.4%
4 1033
 
4.4%
3 215
 
0.9%
2 42
 
0.2%
1 27
 
0.1%

Length

2025-04-17T05:07:11.374559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:11.451001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 22337
94.4%
4 1033
 
4.4%
3 215
 
0.9%
2 42
 
0.2%
1 27
 
0.1%

Most occurring characters

ValueCountFrequency (%)
5 22337
94.4%
4 1033
 
4.4%
3 215
 
0.9%
2 42
 
0.2%
1 27
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 22337
94.4%
4 1033
 
4.4%
3 215
 
0.9%
2 42
 
0.2%
1 27
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 22337
94.4%
4 1033
 
4.4%
3 215
 
0.9%
2 42
 
0.2%
1 27
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 22337
94.4%
4 1033
 
4.4%
3 215
 
0.9%
2 42
 
0.2%
1 27
 
0.1%

TP_046
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
5
22969 
4
 
521
3
 
101
2
 
36
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 22969
97.1%
4 521
 
2.2%
3 101
 
0.4%
2 36
 
0.2%
1 27
 
0.1%

Length

2025-04-17T05:07:11.516807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:11.566706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 22969
97.1%
4 521
 
2.2%
3 101
 
0.4%
2 36
 
0.2%
1 27
 
0.1%

Most occurring characters

ValueCountFrequency (%)
5 22969
97.1%
4 521
 
2.2%
3 101
 
0.4%
2 36
 
0.2%
1 27
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 22969
97.1%
4 521
 
2.2%
3 101
 
0.4%
2 36
 
0.2%
1 27
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 22969
97.1%
4 521
 
2.2%
3 101
 
0.4%
2 36
 
0.2%
1 27
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 22969
97.1%
4 521
 
2.2%
3 101
 
0.4%
2 36
 
0.2%
1 27
 
0.1%

TP_056
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
5
21713 
4
 
976
1
 
425
3
 
326
2
 
214

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 21713
91.8%
4 976
 
4.1%
1 425
 
1.8%
3 326
 
1.4%
2 214
 
0.9%

Length

2025-04-17T05:07:11.628279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:11.683779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 21713
91.8%
4 976
 
4.1%
1 425
 
1.8%
3 326
 
1.4%
2 214
 
0.9%

Most occurring characters

ValueCountFrequency (%)
5 21713
91.8%
4 976
 
4.1%
1 425
 
1.8%
3 326
 
1.4%
2 214
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 21713
91.8%
4 976
 
4.1%
1 425
 
1.8%
3 326
 
1.4%
2 214
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 21713
91.8%
4 976
 
4.1%
1 425
 
1.8%
3 326
 
1.4%
2 214
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 21713
91.8%
4 976
 
4.1%
1 425
 
1.8%
3 326
 
1.4%
2 214
 
0.9%

TP_066
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
5
22990 
4
 
498
3
 
89
1
 
46
2
 
31

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 22990
97.2%
4 498
 
2.1%
3 89
 
0.4%
1 46
 
0.2%
2 31
 
0.1%

Length

2025-04-17T05:07:11.750616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:11.807818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 22990
97.2%
4 498
 
2.1%
3 89
 
0.4%
1 46
 
0.2%
2 31
 
0.1%

Most occurring characters

ValueCountFrequency (%)
5 22990
97.2%
4 498
 
2.1%
3 89
 
0.4%
1 46
 
0.2%
2 31
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 22990
97.2%
4 498
 
2.1%
3 89
 
0.4%
1 46
 
0.2%
2 31
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 22990
97.2%
4 498
 
2.1%
3 89
 
0.4%
1 46
 
0.2%
2 31
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 22990
97.2%
4 498
 
2.1%
3 89
 
0.4%
1 46
 
0.2%
2 31
 
0.1%

TP_086
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
5
23240 
4
 
257
3
 
66
1
 
59
2
 
32

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 23240
98.2%
4 257
 
1.1%
3 66
 
0.3%
1 59
 
0.2%
2 32
 
0.1%

Length

2025-04-17T05:07:11.873588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:11.921529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 23240
98.2%
4 257
 
1.1%
3 66
 
0.3%
1 59
 
0.2%
2 32
 
0.1%

Most occurring characters

ValueCountFrequency (%)
5 23240
98.2%
4 257
 
1.1%
3 66
 
0.3%
1 59
 
0.2%
2 32
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 23240
98.2%
4 257
 
1.1%
3 66
 
0.3%
1 59
 
0.2%
2 32
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 23240
98.2%
4 257
 
1.1%
3 66
 
0.3%
1 59
 
0.2%
2 32
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 23240
98.2%
4 257
 
1.1%
3 66
 
0.3%
1 59
 
0.2%
2 32
 
0.1%

ELC_026a
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
5
18018 
4
2429 
1
 
1518
3
 
932
2
 
757

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row4
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 18018
76.2%
4 2429
 
10.3%
1 1518
 
6.4%
3 932
 
3.9%
2 757
 
3.2%

Length

2025-04-17T05:07:11.984287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:12.036098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 18018
76.2%
4 2429
 
10.3%
1 1518
 
6.4%
3 932
 
3.9%
2 757
 
3.2%

Most occurring characters

ValueCountFrequency (%)
5 18018
76.2%
4 2429
 
10.3%
1 1518
 
6.4%
3 932
 
3.9%
2 757
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 18018
76.2%
4 2429
 
10.3%
1 1518
 
6.4%
3 932
 
3.9%
2 757
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 18018
76.2%
4 2429
 
10.3%
1 1518
 
6.4%
3 932
 
3.9%
2 757
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 18018
76.2%
4 2429
 
10.3%
1 1518
 
6.4%
3 932
 
3.9%
2 757
 
3.2%

ELC_026b
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
5
20214 
4
2756 
3
 
379
2
 
202
1
 
103

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row4
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 20214
85.5%
4 2756
 
11.7%
3 379
 
1.6%
2 202
 
0.9%
1 103
 
0.4%

Length

2025-04-17T05:07:12.101871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:12.155976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 20214
85.5%
4 2756
 
11.7%
3 379
 
1.6%
2 202
 
0.9%
1 103
 
0.4%

Most occurring characters

ValueCountFrequency (%)
5 20214
85.5%
4 2756
 
11.7%
3 379
 
1.6%
2 202
 
0.9%
1 103
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 20214
85.5%
4 2756
 
11.7%
3 379
 
1.6%
2 202
 
0.9%
1 103
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 20214
85.5%
4 2756
 
11.7%
3 379
 
1.6%
2 202
 
0.9%
1 103
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 20214
85.5%
4 2756
 
11.7%
3 379
 
1.6%
2 202
 
0.9%
1 103
 
0.4%

ELC_026c
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
5
21033 
4
 
1697
3
 
431
2
 
277
1
 
216

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row4
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 21033
88.9%
4 1697
 
7.2%
3 431
 
1.8%
2 277
 
1.2%
1 216
 
0.9%

Length

2025-04-17T05:07:12.222469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:12.276934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 21033
88.9%
4 1697
 
7.2%
3 431
 
1.8%
2 277
 
1.2%
1 216
 
0.9%

Most occurring characters

ValueCountFrequency (%)
5 21033
88.9%
4 1697
 
7.2%
3 431
 
1.8%
2 277
 
1.2%
1 216
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 21033
88.9%
4 1697
 
7.2%
3 431
 
1.8%
2 277
 
1.2%
1 216
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 21033
88.9%
4 1697
 
7.2%
3 431
 
1.8%
2 277
 
1.2%
1 216
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 21033
88.9%
4 1697
 
7.2%
3 431
 
1.8%
2 277
 
1.2%
1 216
 
0.9%

VAP_010
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
15490 
3
4096 
2
2101 
1
1967 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row3
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 15490
65.5%
3 4096
 
17.3%
2 2101
 
8.9%
1 1967
 
8.3%

Length

2025-04-17T05:07:12.343063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:12.400360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 15490
65.5%
3 4096
 
17.3%
2 2101
 
8.9%
1 1967
 
8.3%

Most occurring characters

ValueCountFrequency (%)
4 15490
65.5%
3 4096
 
17.3%
2 2101
 
8.9%
1 1967
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 15490
65.5%
3 4096
 
17.3%
2 2101
 
8.9%
1 1967
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 15490
65.5%
3 4096
 
17.3%
2 2101
 
8.9%
1 1967
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 15490
65.5%
3 4096
 
17.3%
2 2101
 
8.9%
1 1967
 
8.3%

CI_010
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7642682
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size369.6 KiB
2025-04-17T05:07:12.452852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3485895
Coefficient of variation (CV)0.76439029
Kurtosis1.2709006
Mean1.7642682
Median Absolute Deviation (MAD)0
Skewness1.5772218
Sum41732
Variance1.8186936
MonotonicityNot monotonic
2025-04-17T05:07:12.513377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 17059
72.1%
3 3616
 
15.3%
5 1601
 
6.8%
4 752
 
3.2%
6 390
 
1.6%
2 236
 
1.0%
ValueCountFrequency (%)
1 17059
72.1%
2 236
 
1.0%
3 3616
 
15.3%
4 752
 
3.2%
5 1601
 
6.8%
6 390
 
1.6%
ValueCountFrequency (%)
6 390
 
1.6%
5 1601
 
6.8%
4 752
 
3.2%
3 3616
 
15.3%
2 236
 
1.0%
1 17059
72.1%

VAP_020
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7620698
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size369.6 KiB
2025-04-17T05:07:12.572404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile6
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0304715
Coefficient of variation (CV)1.1523218
Kurtosis9.5249491
Mean1.7620698
Median Absolute Deviation (MAD)0
Skewness3.1777651
Sum41680
Variance4.1228144
MonotonicityNot monotonic
2025-04-17T05:07:12.628858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 19226
81.3%
3 2521
 
10.7%
10 1020
 
4.3%
6 444
 
1.9%
4 261
 
1.1%
2 68
 
0.3%
9 50
 
0.2%
5 32
 
0.1%
7 19
 
0.1%
8 13
 
0.1%
ValueCountFrequency (%)
1 19226
81.3%
2 68
 
0.3%
3 2521
 
10.7%
4 261
 
1.1%
5 32
 
0.1%
6 444
 
1.9%
7 19
 
0.1%
8 13
 
0.1%
9 50
 
0.2%
10 1020
 
4.3%
ValueCountFrequency (%)
10 1020
 
4.3%
9 50
 
0.2%
8 13
 
0.1%
7 19
 
0.1%
6 444
 
1.9%
5 32
 
0.1%
4 261
 
1.1%
3 2521
 
10.7%
2 68
 
0.3%
1 19226
81.3%

VAP_030
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8199036
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size369.6 KiB
2025-04-17T05:07:12.684822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile7
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.1322962
Coefficient of variation (CV)1.1716534
Kurtosis11.073348
Mean1.8199036
Median Absolute Deviation (MAD)0
Skewness3.3955246
Sum43048
Variance4.5466871
MonotonicityNot monotonic
2025-04-17T05:07:12.761768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 17143
72.5%
2 4065
 
17.2%
5 543
 
2.3%
10 453
 
1.9%
4 443
 
1.9%
11 280
 
1.2%
12 218
 
0.9%
3 176
 
0.7%
8 150
 
0.6%
6 83
 
0.4%
Other values (2) 100
 
0.4%
ValueCountFrequency (%)
1 17143
72.5%
2 4065
 
17.2%
3 176
 
0.7%
4 443
 
1.9%
5 543
 
2.3%
6 83
 
0.4%
7 32
 
0.1%
8 150
 
0.6%
9 68
 
0.3%
10 453
 
1.9%
ValueCountFrequency (%)
12 218
0.9%
11 280
1.2%
10 453
1.9%
9 68
 
0.3%
8 150
 
0.6%
7 32
 
0.1%
6 83
 
0.4%
5 543
2.3%
4 443
1.9%
3 176
 
0.7%

VAP_040
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9891773
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size369.6 KiB
2025-04-17T05:07:12.838431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6392575
Coefficient of variation (CV)1.3268086
Kurtosis5.2488264
Mean1.9891773
Median Absolute Deviation (MAD)0
Skewness2.6041439
Sum47052
Variance6.96568
MonotonicityNot monotonic
2025-04-17T05:07:12.900754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 19837
83.9%
10 680
 
2.9%
2 636
 
2.7%
9 614
 
2.6%
8 447
 
1.9%
4 397
 
1.7%
11 365
 
1.5%
3 284
 
1.2%
12 211
 
0.9%
5 79
 
0.3%
Other values (2) 104
 
0.4%
ValueCountFrequency (%)
1 19837
83.9%
2 636
 
2.7%
3 284
 
1.2%
4 397
 
1.7%
5 79
 
0.3%
6 69
 
0.3%
7 35
 
0.1%
8 447
 
1.9%
9 614
 
2.6%
10 680
 
2.9%
ValueCountFrequency (%)
12 211
 
0.9%
11 365
1.5%
10 680
2.9%
9 614
2.6%
8 447
1.9%
7 35
 
0.1%
6 69
 
0.3%
5 79
 
0.3%
4 397
1.7%
3 284
1.2%

VAP_050a
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8859812
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size369.6 KiB
2025-04-17T05:07:12.959165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile7
Maximum11
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.183633
Coefficient of variation (CV)1.1578233
Kurtosis4.6231339
Mean1.8859812
Median Absolute Deviation (MAD)0
Skewness2.3985208
Sum44611
Variance4.7682531
MonotonicityNot monotonic
2025-04-17T05:07:13.017301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 19690
83.2%
7 1672
 
7.1%
4 725
 
3.1%
8 499
 
2.1%
2 347
 
1.5%
11 274
 
1.2%
6 197
 
0.8%
3 93
 
0.4%
5 73
 
0.3%
9 49
 
0.2%
ValueCountFrequency (%)
1 19690
83.2%
2 347
 
1.5%
3 93
 
0.4%
4 725
 
3.1%
5 73
 
0.3%
6 197
 
0.8%
7 1672
 
7.1%
8 499
 
2.1%
9 49
 
0.2%
10 35
 
0.1%
ValueCountFrequency (%)
11 274
 
1.2%
10 35
 
0.1%
9 49
 
0.2%
8 499
 
2.1%
7 1672
7.1%
6 197
 
0.8%
5 73
 
0.3%
4 725
3.1%
3 93
 
0.4%
2 347
 
1.5%

VAP_050b
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8620529
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size369.6 KiB
2025-04-17T05:07:13.073469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile7
Maximum11
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1683167
Coefficient of variation (CV)1.1644764
Kurtosis5.0521141
Mean1.8620529
Median Absolute Deviation (MAD)0
Skewness2.4758067
Sum44045
Variance4.7015972
MonotonicityNot monotonic
2025-04-17T05:07:13.130651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 19799
83.7%
7 1556
 
6.6%
4 727
 
3.1%
8 509
 
2.2%
2 341
 
1.4%
11 297
 
1.3%
6 188
 
0.8%
3 104
 
0.4%
5 61
 
0.3%
9 40
 
0.2%
ValueCountFrequency (%)
1 19799
83.7%
2 341
 
1.4%
3 104
 
0.4%
4 727
 
3.1%
5 61
 
0.3%
6 188
 
0.8%
7 1556
 
6.6%
8 509
 
2.2%
9 40
 
0.2%
10 32
 
0.1%
ValueCountFrequency (%)
11 297
 
1.3%
10 32
 
0.1%
9 40
 
0.2%
8 509
 
2.2%
7 1556
6.6%
6 188
 
0.8%
5 61
 
0.3%
4 727
3.1%
3 104
 
0.4%
2 341
 
1.4%

VAP_060
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2909022
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size369.6 KiB
2025-04-17T05:07:13.194874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1881918
Coefficient of variation (CV)0.51865674
Kurtosis3.4730024
Mean2.2909022
Median Absolute Deviation (MAD)0
Skewness1.9577954
Sum54189
Variance1.4117999
MonotonicityNot monotonic
2025-04-17T05:07:13.263285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 15739
66.5%
1 3528
 
14.9%
3 1755
 
7.4%
6 1317
 
5.6%
5 756
 
3.2%
4 559
 
2.4%
ValueCountFrequency (%)
1 3528
 
14.9%
2 15739
66.5%
3 1755
 
7.4%
4 559
 
2.4%
5 756
 
3.2%
6 1317
 
5.6%
ValueCountFrequency (%)
6 1317
 
5.6%
5 756
 
3.2%
4 559
 
2.4%
3 1755
 
7.4%
2 15739
66.5%
1 3528
 
14.9%

ALC_010
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
12727 
1
10927 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 12727
53.8%
1 10927
46.2%

Length

2025-04-17T05:07:13.334770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:13.384543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 12727
53.8%
1 10927
46.2%

Most occurring characters

ValueCountFrequency (%)
2 12727
53.8%
1 10927
46.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 12727
53.8%
1 10927
46.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 12727
53.8%
1 10927
46.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 12727
53.8%
1 10927
46.2%

NRG_010
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
15443 
1
8211 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 15443
65.3%
1 8211
34.7%

Length

2025-04-17T05:07:13.436605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:13.480425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 15443
65.3%
1 8211
34.7%

Most occurring characters

ValueCountFrequency (%)
2 15443
65.3%
1 8211
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 15443
65.3%
1 8211
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 15443
65.3%
1 8211
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 15443
65.3%
1 8211
34.7%

NRG_020
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
21098 
1
2556 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 21098
89.2%
1 2556
 
10.8%

Length

2025-04-17T05:07:13.534990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:13.575675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 21098
89.2%
1 2556
 
10.8%

Most occurring characters

ValueCountFrequency (%)
2 21098
89.2%
1 2556
 
10.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 21098
89.2%
1 2556
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 21098
89.2%
1 2556
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 21098
89.2%
1 2556
 
10.8%

NRG_030
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
22277 
1
 
1377

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 22277
94.2%
1 1377
 
5.8%

Length

2025-04-17T05:07:13.628157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:13.669972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 22277
94.2%
1 1377
 
5.8%

Most occurring characters

ValueCountFrequency (%)
2 22277
94.2%
1 1377
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 22277
94.2%
1 1377
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 22277
94.2%
1 1377
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 22277
94.2%
1 1377
 
5.8%

NRG_040
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
22342 
1
 
1312

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 22342
94.5%
1 1312
 
5.5%

Length

2025-04-17T05:07:13.721477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:13.763710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 22342
94.5%
1 1312
 
5.5%

Most occurring characters

ValueCountFrequency (%)
2 22342
94.5%
1 1312
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 22342
94.5%
1 1312
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 22342
94.5%
1 1312
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 22342
94.5%
1 1312
 
5.5%

NRG_050
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
19899 
1
3755 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 19899
84.1%
1 3755
 
15.9%

Length

2025-04-17T05:07:13.813681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:13.863931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 19899
84.1%
1 3755
 
15.9%

Most occurring characters

ValueCountFrequency (%)
2 19899
84.1%
1 3755
 
15.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 19899
84.1%
1 3755
 
15.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 19899
84.1%
1 3755
 
15.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 19899
84.1%
1 3755
 
15.9%

CAN_010
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
19330 
1
4324 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 19330
81.7%
1 4324
 
18.3%

Length

2025-04-17T05:07:13.916636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:13.958377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 19330
81.7%
1 4324
 
18.3%

Most occurring characters

ValueCountFrequency (%)
2 19330
81.7%
1 4324
 
18.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 19330
81.7%
1 4324
 
18.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 19330
81.7%
1 4324
 
18.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 19330
81.7%
1 4324
 
18.3%

CAN_130
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
20532 
4
 
1777
2
 
1260
3
 
85

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row4
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 20532
86.8%
4 1777
 
7.5%
2 1260
 
5.3%
3 85
 
0.4%

Length

2025-04-17T05:07:14.011837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:14.921291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 20532
86.8%
4 1777
 
7.5%
2 1260
 
5.3%
3 85
 
0.4%

Most occurring characters

ValueCountFrequency (%)
1 20532
86.8%
4 1777
 
7.5%
2 1260
 
5.3%
3 85
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 20532
86.8%
4 1777
 
7.5%
2 1260
 
5.3%
3 85
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 20532
86.8%
4 1777
 
7.5%
2 1260
 
5.3%
3 85
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 20532
86.8%
4 1777
 
7.5%
2 1260
 
5.3%
3 85
 
0.4%

CAN_140
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3500888
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size369.6 KiB
2025-04-17T05:07:14.992364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1207847
Coefficient of variation (CV)0.83015632
Kurtosis21.574762
Mean1.3500888
Median Absolute Deviation (MAD)0
Skewness4.2959729
Sum31935
Variance1.2561584
MonotonicityNot monotonic
2025-04-17T05:07:15.116309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 20599
87.1%
3 1357
 
5.7%
2 629
 
2.7%
4 405
 
1.7%
5 304
 
1.3%
9 205
 
0.9%
6 82
 
0.3%
7 54
 
0.2%
8 19
 
0.1%
ValueCountFrequency (%)
1 20599
87.1%
2 629
 
2.7%
3 1357
 
5.7%
4 405
 
1.7%
5 304
 
1.3%
6 82
 
0.3%
7 54
 
0.2%
8 19
 
0.1%
9 205
 
0.9%
ValueCountFrequency (%)
9 205
 
0.9%
8 19
 
0.1%
7 54
 
0.2%
6 82
 
0.3%
5 304
 
1.3%
4 405
 
1.7%
3 1357
 
5.7%
2 629
 
2.7%
1 20599
87.1%

UND_010
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23068 
2
 
340
3
 
246

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23068
97.5%
2 340
 
1.4%
3 246
 
1.0%

Length

2025-04-17T05:07:15.182007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:15.226099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23068
97.5%
2 340
 
1.4%
3 246
 
1.0%

Most occurring characters

ValueCountFrequency (%)
1 23068
97.5%
2 340
 
1.4%
3 246
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23068
97.5%
2 340
 
1.4%
3 246
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23068
97.5%
2 340
 
1.4%
3 246
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23068
97.5%
2 340
 
1.4%
3 246
 
1.0%

UND_020
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23150 
2
 
267
3
 
237

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23150
97.9%
2 267
 
1.1%
3 237
 
1.0%

Length

2025-04-17T05:07:15.279499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:15.322560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23150
97.9%
2 267
 
1.1%
3 237
 
1.0%

Most occurring characters

ValueCountFrequency (%)
1 23150
97.9%
2 267
 
1.1%
3 237
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23150
97.9%
2 267
 
1.1%
3 237
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23150
97.9%
2 267
 
1.1%
3 237
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23150
97.9%
2 267
 
1.1%
3 237
 
1.0%

MET_010
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23491 
2
 
94
3
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23491
99.3%
2 94
 
0.4%
3 69
 
0.3%

Length

2025-04-17T05:07:15.378189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:15.426090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23491
99.3%
2 94
 
0.4%
3 69
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1 23491
99.3%
2 94
 
0.4%
3 69
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23491
99.3%
2 94
 
0.4%
3 69
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23491
99.3%
2 94
 
0.4%
3 69
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23491
99.3%
2 94
 
0.4%
3 69
 
0.3%

XTC_010
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23384 
2
 
157
3
 
113

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23384
98.9%
2 157
 
0.7%
3 113
 
0.5%

Length

2025-04-17T05:07:15.482130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:15.533523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23384
98.9%
2 157
 
0.7%
3 113
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1 23384
98.9%
2 157
 
0.7%
3 113
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23384
98.9%
2 157
 
0.7%
3 113
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23384
98.9%
2 157
 
0.7%
3 113
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23384
98.9%
2 157
 
0.7%
3 113
 
0.5%

HAL_010
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
22841 
2
 
602
3
 
211

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 22841
96.6%
2 602
 
2.5%
3 211
 
0.9%

Length

2025-04-17T05:07:15.590583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:15.635930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 22841
96.6%
2 602
 
2.5%
3 211
 
0.9%

Most occurring characters

ValueCountFrequency (%)
1 22841
96.6%
2 602
 
2.5%
3 211
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 22841
96.6%
2 602
 
2.5%
3 211
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 22841
96.6%
2 602
 
2.5%
3 211
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 22841
96.6%
2 602
 
2.5%
3 211
 
0.9%

HER_010
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23554 
2
 
58
3
 
42

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23554
99.6%
2 58
 
0.2%
3 42
 
0.2%

Length

2025-04-17T05:07:15.763246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:15.838991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23554
99.6%
2 58
 
0.2%
3 42
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 23554
99.6%
2 58
 
0.2%
3 42
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23554
99.6%
2 58
 
0.2%
3 42
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23554
99.6%
2 58
 
0.2%
3 42
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23554
99.6%
2 58
 
0.2%
3 42
 
0.2%

COC_010
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23363 
2
 
169
3
 
122

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23363
98.8%
2 169
 
0.7%
3 122
 
0.5%

Length

2025-04-17T05:07:15.905391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:15.953329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23363
98.8%
2 169
 
0.7%
3 122
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1 23363
98.8%
2 169
 
0.7%
3 122
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23363
98.8%
2 169
 
0.7%
3 122
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23363
98.8%
2 169
 
0.7%
3 122
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23363
98.8%
2 169
 
0.7%
3 122
 
0.5%

SYN_010
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
22984 
2
 
534
3
 
136

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 22984
97.2%
2 534
 
2.3%
3 136
 
0.6%

Length

2025-04-17T05:07:16.007336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:16.052574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 22984
97.2%
2 534
 
2.3%
3 136
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 22984
97.2%
2 534
 
2.3%
3 136
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 22984
97.2%
2 534
 
2.3%
3 136
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 22984
97.2%
2 534
 
2.3%
3 136
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 22984
97.2%
2 534
 
2.3%
3 136
 
0.6%

BZP_010
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23574 
2
 
47
3
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23574
99.7%
2 47
 
0.2%
3 33
 
0.1%

Length

2025-04-17T05:07:16.104637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:16.153723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23574
99.7%
2 47
 
0.2%
3 33
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 23574
99.7%
2 47
 
0.2%
3 33
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23574
99.7%
2 47
 
0.2%
3 33
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23574
99.7%
2 47
 
0.2%
3 33
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23574
99.7%
2 47
 
0.2%
3 33
 
0.1%

BS_010
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23510 
2
 
93
3
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23510
99.4%
2 93
 
0.4%
3 51
 
0.2%

Length

2025-04-17T05:07:16.212601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:16.259098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23510
99.4%
2 93
 
0.4%
3 51
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 23510
99.4%
2 93
 
0.4%
3 51
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23510
99.4%
2 93
 
0.4%
3 51
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23510
99.4%
2 93
 
0.4%
3 51
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23510
99.4%
2 93
 
0.4%
3 51
 
0.2%

TNB_010
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23579 
2
 
42
3
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23579
99.7%
2 42
 
0.2%
3 33
 
0.1%

Length

2025-04-17T05:07:16.316387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:16.364318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23579
99.7%
2 42
 
0.2%
3 33
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 23579
99.7%
2 42
 
0.2%
3 33
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23579
99.7%
2 42
 
0.2%
3 33
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23579
99.7%
2 42
 
0.2%
3 33
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23579
99.7%
2 42
 
0.2%
3 33
 
0.1%

TRP_010
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23542 
2
 
77
3
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23542
99.5%
2 77
 
0.3%
3 35
 
0.1%

Length

2025-04-17T05:07:16.425949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:16.470397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23542
99.5%
2 77
 
0.3%
3 35
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 23542
99.5%
2 77
 
0.3%
3 35
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23542
99.5%
2 77
 
0.3%
3 35
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23542
99.5%
2 77
 
0.3%
3 35
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23542
99.5%
2 77
 
0.3%
3 35
 
0.1%

GLU_010
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23298 
2
 
181
3
 
175

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23298
98.5%
2 181
 
0.8%
3 175
 
0.7%

Length

2025-04-17T05:07:16.524035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:16.565544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23298
98.5%
2 181
 
0.8%
3 175
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 23298
98.5%
2 181
 
0.8%
3 175
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23298
98.5%
2 181
 
0.8%
3 175
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23298
98.5%
2 181
 
0.8%
3 175
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23298
98.5%
2 181
 
0.8%
3 175
 
0.7%

SAL_010
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23533 
2
 
68
3
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23533
99.5%
2 68
 
0.3%
3 53
 
0.2%

Length

2025-04-17T05:07:16.617411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:16.658710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23533
99.5%
2 68
 
0.3%
3 53
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 23533
99.5%
2 68
 
0.3%
3 53
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23533
99.5%
2 68
 
0.3%
3 53
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23533
99.5%
2 68
 
0.3%
3 53
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23533
99.5%
2 68
 
0.3%
3 53
 
0.2%

SLP_010
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
22441 
2
 
876
3
 
337

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 22441
94.9%
2 876
 
3.7%
3 337
 
1.4%

Length

2025-04-17T05:07:16.709380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:16.753865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 22441
94.9%
2 876
 
3.7%
3 337
 
1.4%

Most occurring characters

ValueCountFrequency (%)
1 22441
94.9%
2 876
 
3.7%
3 337
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 22441
94.9%
2 876
 
3.7%
3 337
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 22441
94.9%
2 876
 
3.7%
3 337
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 22441
94.9%
2 876
 
3.7%
3 337
 
1.4%

STI_030
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23116 
2
 
397
3
 
141

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23116
97.7%
2 397
 
1.7%
3 141
 
0.6%

Length

2025-04-17T05:07:16.805308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:16.853100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23116
97.7%
2 397
 
1.7%
3 141
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 23116
97.7%
2 397
 
1.7%
3 141
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23116
97.7%
2 397
 
1.7%
3 141
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23116
97.7%
2 397
 
1.7%
3 141
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23116
97.7%
2 397
 
1.7%
3 141
 
0.6%

DEX_010
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
22236 
2
 
1059
3
 
359

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 22236
94.0%
2 1059
 
4.5%
3 359
 
1.5%

Length

2025-04-17T05:07:16.912667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:16.964618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 22236
94.0%
2 1059
 
4.5%
3 359
 
1.5%

Most occurring characters

ValueCountFrequency (%)
1 22236
94.0%
2 1059
 
4.5%
3 359
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 22236
94.0%
2 1059
 
4.5%
3 359
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 22236
94.0%
2 1059
 
4.5%
3 359
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 22236
94.0%
2 1059
 
4.5%
3 359
 
1.5%

GRV_010
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
22496 
2
 
782
3
 
376

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 22496
95.1%
2 782
 
3.3%
3 376
 
1.6%

Length

2025-04-17T05:07:17.023352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:17.072110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 22496
95.1%
2 782
 
3.3%
3 376
 
1.6%

Most occurring characters

ValueCountFrequency (%)
1 22496
95.1%
2 782
 
3.3%
3 376
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 22496
95.1%
2 782
 
3.3%
3 376
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 22496
95.1%
2 782
 
3.3%
3 376
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 22496
95.1%
2 782
 
3.3%
3 376
 
1.6%

STI_080
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
21003 
1
 
1793
3
 
858

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 21003
88.8%
1 1793
 
7.6%
3 858
 
3.6%

Length

2025-04-17T05:07:17.127886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:17.172802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 21003
88.8%
1 1793
 
7.6%
3 858
 
3.6%

Most occurring characters

ValueCountFrequency (%)
2 21003
88.8%
1 1793
 
7.6%
3 858
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 21003
88.8%
1 1793
 
7.6%
3 858
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 21003
88.8%
1 1793
 
7.6%
3 858
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 21003
88.8%
1 1793
 
7.6%
3 858
 
3.6%

STI_050
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23013 
2
 
434
3
 
207

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23013
97.3%
2 434
 
1.8%
3 207
 
0.9%

Length

2025-04-17T05:07:17.226385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:17.271863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23013
97.3%
2 434
 
1.8%
3 207
 
0.9%

Most occurring characters

ValueCountFrequency (%)
1 23013
97.3%
2 434
 
1.8%
3 207
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23013
97.3%
2 434
 
1.8%
3 207
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23013
97.3%
2 434
 
1.8%
3 207
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23013
97.3%
2 434
 
1.8%
3 207
 
0.9%

SED_050
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
21995 
1
 
1023
3
 
636

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 21995
93.0%
1 1023
 
4.3%
3 636
 
2.7%

Length

2025-04-17T05:07:17.330864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:17.381363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 21995
93.0%
1 1023
 
4.3%
3 636
 
2.7%

Most occurring characters

ValueCountFrequency (%)
2 21995
93.0%
1 1023
 
4.3%
3 636
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 21995
93.0%
1 1023
 
4.3%
3 636
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 21995
93.0%
1 1023
 
4.3%
3 636
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 21995
93.0%
1 1023
 
4.3%
3 636
 
2.7%

SED_030
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23289 
2
 
259
3
 
106

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23289
98.5%
2 259
 
1.1%
3 106
 
0.4%

Length

2025-04-17T05:07:17.436171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:17.481412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23289
98.5%
2 259
 
1.1%
3 106
 
0.4%

Most occurring characters

ValueCountFrequency (%)
1 23289
98.5%
2 259
 
1.1%
3 106
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23289
98.5%
2 259
 
1.1%
3 106
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23289
98.5%
2 259
 
1.1%
3 106
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23289
98.5%
2 259
 
1.1%
3 106
 
0.4%

PR_100
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
21383 
1
 
1603
3
 
668

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 21383
90.4%
1 1603
 
6.8%
3 668
 
2.8%

Length

2025-04-17T05:07:17.537672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:17.584021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 21383
90.4%
1 1603
 
6.8%
3 668
 
2.8%

Most occurring characters

ValueCountFrequency (%)
2 21383
90.4%
1 1603
 
6.8%
3 668
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 21383
90.4%
1 1603
 
6.8%
3 668
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 21383
90.4%
1 1603
 
6.8%
3 668
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 21383
90.4%
1 1603
 
6.8%
3 668
 
2.8%

PR_030
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23457 
2
 
115
3
 
82

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23457
99.2%
2 115
 
0.5%
3 82
 
0.3%

Length

2025-04-17T05:07:17.639769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:17.684614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23457
99.2%
2 115
 
0.5%
3 82
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1 23457
99.2%
2 115
 
0.5%
3 82
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23457
99.2%
2 115
 
0.5%
3 82
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23457
99.2%
2 115
 
0.5%
3 82
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23457
99.2%
2 115
 
0.5%
3 82
 
0.3%

PR_050
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23529 
2
 
86
3
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23529
99.5%
2 86
 
0.4%
3 39
 
0.2%

Length

2025-04-17T05:07:17.743108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:17.787983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23529
99.5%
2 86
 
0.4%
3 39
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 23529
99.5%
2 86
 
0.4%
3 39
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23529
99.5%
2 86
 
0.4%
3 39
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23529
99.5%
2 86
 
0.4%
3 39
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23529
99.5%
2 86
 
0.4%
3 39
 
0.2%

PR_060
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23181 
2
 
299
3
 
174

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23181
98.0%
2 299
 
1.3%
3 174
 
0.7%

Length

2025-04-17T05:07:17.842860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:17.889063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23181
98.0%
2 299
 
1.3%
3 174
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 23181
98.0%
2 299
 
1.3%
3 174
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23181
98.0%
2 299
 
1.3%
3 174
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23181
98.0%
2 299
 
1.3%
3 174
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23181
98.0%
2 299
 
1.3%
3 174
 
0.7%

PR_110
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0963896
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size369.6 KiB
2025-04-17T05:07:17.944025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.70847261
Coefficient of variation (CV)0.6461869
Kurtosis85.351096
Mean1.0963896
Median Absolute Deviation (MAD)0
Skewness8.9483712
Sum25934
Variance0.50193344
MonotonicityNot monotonic
2025-04-17T05:07:18.010620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 22962
97.1%
2 276
 
1.2%
3 100
 
0.4%
9 99
 
0.4%
7 73
 
0.3%
4 71
 
0.3%
6 57
 
0.2%
5 12
 
0.1%
8 4
 
< 0.1%
ValueCountFrequency (%)
1 22962
97.1%
2 276
 
1.2%
3 100
 
0.4%
4 71
 
0.3%
5 12
 
0.1%
6 57
 
0.2%
7 73
 
0.3%
8 4
 
< 0.1%
9 99
 
0.4%
ValueCountFrequency (%)
9 99
 
0.4%
8 4
 
< 0.1%
7 73
 
0.3%
6 57
 
0.2%
5 12
 
0.1%
4 71
 
0.3%
3 100
 
0.4%
2 276
 
1.2%
1 22962
97.1%

POLY_010
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23547 
2
 
32
4
 
26
5
 
25
3
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23547
99.5%
2 32
 
0.1%
4 26
 
0.1%
5 25
 
0.1%
3 24
 
0.1%

Length

2025-04-17T05:07:18.082333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:18.133113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23547
99.5%
2 32
 
0.1%
4 26
 
0.1%
5 25
 
0.1%
3 24
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 23547
99.5%
2 32
 
0.1%
4 26
 
0.1%
5 25
 
0.1%
3 24
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23547
99.5%
2 32
 
0.1%
4 26
 
0.1%
5 25
 
0.1%
3 24
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23547
99.5%
2 32
 
0.1%
4 26
 
0.1%
5 25
 
0.1%
3 24
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23547
99.5%
2 32
 
0.1%
4 26
 
0.1%
5 25
 
0.1%
3 24
 
0.1%

POLY_020
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23503 
4
 
56
2
 
46
3
 
28
5
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23503
99.4%
4 56
 
0.2%
2 46
 
0.2%
3 28
 
0.1%
5 21
 
0.1%

Length

2025-04-17T05:07:18.196551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:18.248784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23503
99.4%
4 56
 
0.2%
2 46
 
0.2%
3 28
 
0.1%
5 21
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 23503
99.4%
4 56
 
0.2%
2 46
 
0.2%
3 28
 
0.1%
5 21
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23503
99.4%
4 56
 
0.2%
2 46
 
0.2%
3 28
 
0.1%
5 21
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23503
99.4%
4 56
 
0.2%
2 46
 
0.2%
3 28
 
0.1%
5 21
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23503
99.4%
4 56
 
0.2%
2 46
 
0.2%
3 28
 
0.1%
5 21
 
0.1%

POLY_030
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23273 
2
 
158
4
 
133
3
 
60
5
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23273
98.4%
2 158
 
0.7%
4 133
 
0.6%
3 60
 
0.3%
5 30
 
0.1%

Length

2025-04-17T05:07:18.314067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:18.371759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23273
98.4%
2 158
 
0.7%
4 133
 
0.6%
3 60
 
0.3%
5 30
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 23273
98.4%
2 158
 
0.7%
4 133
 
0.6%
3 60
 
0.3%
5 30
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23273
98.4%
2 158
 
0.7%
4 133
 
0.6%
3 60
 
0.3%
5 30
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23273
98.4%
2 158
 
0.7%
4 133
 
0.6%
3 60
 
0.3%
5 30
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23273
98.4%
2 158
 
0.7%
4 133
 
0.6%
3 60
 
0.3%
5 30
 
0.1%

POLY_040
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23589 
5
 
23
3
 
22
2
 
13
4
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23589
99.7%
5 23
 
0.1%
3 22
 
0.1%
2 13
 
0.1%
4 7
 
< 0.1%

Length

2025-04-17T05:07:18.435784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:18.492027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23589
99.7%
5 23
 
0.1%
3 22
 
0.1%
2 13
 
0.1%
4 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 23589
99.7%
5 23
 
0.1%
3 22
 
0.1%
2 13
 
0.1%
4 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23589
99.7%
5 23
 
0.1%
3 22
 
0.1%
2 13
 
0.1%
4 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23589
99.7%
5 23
 
0.1%
3 22
 
0.1%
2 13
 
0.1%
4 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23589
99.7%
5 23
 
0.1%
3 22
 
0.1%
2 13
 
0.1%
4 7
 
< 0.1%

POLY_050
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23469 
4
 
60
2
 
52
3
 
46
5
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23469
99.2%
4 60
 
0.3%
2 52
 
0.2%
3 46
 
0.2%
5 27
 
0.1%

Length

2025-04-17T05:07:18.559353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:18.609539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23469
99.2%
4 60
 
0.3%
2 52
 
0.2%
3 46
 
0.2%
5 27
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 23469
99.2%
4 60
 
0.3%
2 52
 
0.2%
3 46
 
0.2%
5 27
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23469
99.2%
4 60
 
0.3%
2 52
 
0.2%
3 46
 
0.2%
5 27
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23469
99.2%
4 60
 
0.3%
2 52
 
0.2%
3 46
 
0.2%
5 27
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23469
99.2%
4 60
 
0.3%
2 52
 
0.2%
3 46
 
0.2%
5 27
 
0.1%

POLY_060
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23344 
2
 
95
3
 
81
4
 
81
5
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23344
98.7%
2 95
 
0.4%
3 81
 
0.3%
4 81
 
0.3%
5 53
 
0.2%

Length

2025-04-17T05:07:18.672344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:18.721326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23344
98.7%
2 95
 
0.4%
3 81
 
0.3%
4 81
 
0.3%
5 53
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 23344
98.7%
2 95
 
0.4%
3 81
 
0.3%
4 81
 
0.3%
5 53
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23344
98.7%
2 95
 
0.4%
3 81
 
0.3%
4 81
 
0.3%
5 53
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23344
98.7%
2 95
 
0.4%
3 81
 
0.3%
4 81
 
0.3%
5 53
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23344
98.7%
2 95
 
0.4%
3 81
 
0.3%
4 81
 
0.3%
5 53
 
0.2%

POLY_070
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23480 
2
 
54
4
 
50
3
 
41
5
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23480
99.3%
2 54
 
0.2%
4 50
 
0.2%
3 41
 
0.2%
5 29
 
0.1%

Length

2025-04-17T05:07:18.783974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:18.832401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23480
99.3%
2 54
 
0.2%
4 50
 
0.2%
3 41
 
0.2%
5 29
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 23480
99.3%
2 54
 
0.2%
4 50
 
0.2%
3 41
 
0.2%
5 29
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23480
99.3%
2 54
 
0.2%
4 50
 
0.2%
3 41
 
0.2%
5 29
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23480
99.3%
2 54
 
0.2%
4 50
 
0.2%
3 41
 
0.2%
5 29
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23480
99.3%
2 54
 
0.2%
4 50
 
0.2%
3 41
 
0.2%
5 29
 
0.1%

POLY_080
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23422 
2
 
77
4
 
68
5
 
45
3
 
42

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23422
99.0%
2 77
 
0.3%
4 68
 
0.3%
5 45
 
0.2%
3 42
 
0.2%

Length

2025-04-17T05:07:18.891689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:18.944148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23422
99.0%
2 77
 
0.3%
4 68
 
0.3%
5 45
 
0.2%
3 42
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 23422
99.0%
2 77
 
0.3%
4 68
 
0.3%
5 45
 
0.2%
3 42
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23422
99.0%
2 77
 
0.3%
4 68
 
0.3%
5 45
 
0.2%
3 42
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23422
99.0%
2 77
 
0.3%
4 68
 
0.3%
5 45
 
0.2%
3 42
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23422
99.0%
2 77
 
0.3%
4 68
 
0.3%
5 45
 
0.2%
3 42
 
0.2%

POLY_090
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23313 
2
 
149
4
 
83
3
 
65
5
 
44

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23313
98.6%
2 149
 
0.6%
4 83
 
0.4%
3 65
 
0.3%
5 44
 
0.2%

Length

2025-04-17T05:07:19.013592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:19.063413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23313
98.6%
2 149
 
0.6%
4 83
 
0.4%
3 65
 
0.3%
5 44
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 23313
98.6%
2 149
 
0.6%
4 83
 
0.4%
3 65
 
0.3%
5 44
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23313
98.6%
2 149
 
0.6%
4 83
 
0.4%
3 65
 
0.3%
5 44
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23313
98.6%
2 149
 
0.6%
4 83
 
0.4%
3 65
 
0.3%
5 44
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23313
98.6%
2 149
 
0.6%
4 83
 
0.4%
3 65
 
0.3%
5 44
 
0.2%

POLY_100
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23457 
2
 
78
4
 
47
3
 
45
5
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23457
99.2%
2 78
 
0.3%
4 47
 
0.2%
3 45
 
0.2%
5 27
 
0.1%

Length

2025-04-17T05:07:19.124494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:19.174035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23457
99.2%
2 78
 
0.3%
4 47
 
0.2%
3 45
 
0.2%
5 27
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 23457
99.2%
2 78
 
0.3%
4 47
 
0.2%
3 45
 
0.2%
5 27
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23457
99.2%
2 78
 
0.3%
4 47
 
0.2%
3 45
 
0.2%
5 27
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23457
99.2%
2 78
 
0.3%
4 47
 
0.2%
3 45
 
0.2%
5 27
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23457
99.2%
2 78
 
0.3%
4 47
 
0.2%
3 45
 
0.2%
5 27
 
0.1%

POLY_110
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23343 
2
 
122
4
 
80
5
 
55
3
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23343
98.7%
2 122
 
0.5%
4 80
 
0.3%
5 55
 
0.2%
3 54
 
0.2%

Length

2025-04-17T05:07:19.237409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:19.289060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23343
98.7%
2 122
 
0.5%
4 80
 
0.3%
5 55
 
0.2%
3 54
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 23343
98.7%
2 122
 
0.5%
4 80
 
0.3%
5 55
 
0.2%
3 54
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23343
98.7%
2 122
 
0.5%
4 80
 
0.3%
5 55
 
0.2%
3 54
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23343
98.7%
2 122
 
0.5%
4 80
 
0.3%
5 55
 
0.2%
3 54
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23343
98.7%
2 122
 
0.5%
4 80
 
0.3%
5 55
 
0.2%
3 54
 
0.2%

POLY_120
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23221 
2
 
161
4
 
131
5
 
77
3
 
64

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23221
98.2%
2 161
 
0.7%
4 131
 
0.6%
5 77
 
0.3%
3 64
 
0.3%

Length

2025-04-17T05:07:19.350211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:19.406376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23221
98.2%
2 161
 
0.7%
4 131
 
0.6%
5 77
 
0.3%
3 64
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1 23221
98.2%
2 161
 
0.7%
4 131
 
0.6%
5 77
 
0.3%
3 64
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23221
98.2%
2 161
 
0.7%
4 131
 
0.6%
5 77
 
0.3%
3 64
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23221
98.2%
2 161
 
0.7%
4 131
 
0.6%
5 77
 
0.3%
3 64
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23221
98.2%
2 161
 
0.7%
4 131
 
0.6%
5 77
 
0.3%
3 64
 
0.3%

POLY_130
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23561 
5
 
28
3
 
24
4
 
21
2
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23561
99.6%
5 28
 
0.1%
3 24
 
0.1%
4 21
 
0.1%
2 20
 
0.1%

Length

2025-04-17T05:07:19.465633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:19.515004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23561
99.6%
5 28
 
0.1%
3 24
 
0.1%
4 21
 
0.1%
2 20
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 23561
99.6%
5 28
 
0.1%
3 24
 
0.1%
4 21
 
0.1%
2 20
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23561
99.6%
5 28
 
0.1%
3 24
 
0.1%
4 21
 
0.1%
2 20
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23561
99.6%
5 28
 
0.1%
3 24
 
0.1%
4 21
 
0.1%
2 20
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23561
99.6%
5 28
 
0.1%
3 24
 
0.1%
4 21
 
0.1%
2 20
 
0.1%

POLY_140
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23543 
4
 
32
2
 
30
5
 
27
3
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23543
99.5%
4 32
 
0.1%
2 30
 
0.1%
5 27
 
0.1%
3 22
 
0.1%

Length

2025-04-17T05:07:19.575399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:19.621910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23543
99.5%
4 32
 
0.1%
2 30
 
0.1%
5 27
 
0.1%
3 22
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 23543
99.5%
4 32
 
0.1%
2 30
 
0.1%
5 27
 
0.1%
3 22
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23543
99.5%
4 32
 
0.1%
2 30
 
0.1%
5 27
 
0.1%
3 22
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23543
99.5%
4 32
 
0.1%
2 30
 
0.1%
5 27
 
0.1%
3 22
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23543
99.5%
4 32
 
0.1%
2 30
 
0.1%
5 27
 
0.1%
3 22
 
0.1%

POLY_150
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23530 
4
 
35
2
 
32
3
 
30
5
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23530
99.5%
4 35
 
0.1%
2 32
 
0.1%
3 30
 
0.1%
5 27
 
0.1%

Length

2025-04-17T05:07:19.684706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:19.733223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23530
99.5%
4 35
 
0.1%
2 32
 
0.1%
3 30
 
0.1%
5 27
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 23530
99.5%
4 35
 
0.1%
2 32
 
0.1%
3 30
 
0.1%
5 27
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23530
99.5%
4 35
 
0.1%
2 32
 
0.1%
3 30
 
0.1%
5 27
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23530
99.5%
4 35
 
0.1%
2 32
 
0.1%
3 30
 
0.1%
5 27
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23530
99.5%
4 35
 
0.1%
2 32
 
0.1%
3 30
 
0.1%
5 27
 
0.1%

POLY_160
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23464 
2
 
56
3
 
53
5
 
43
4
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23464
99.2%
2 56
 
0.2%
3 53
 
0.2%
5 43
 
0.2%
4 38
 
0.2%

Length

2025-04-17T05:07:19.792164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:19.840433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23464
99.2%
2 56
 
0.2%
3 53
 
0.2%
5 43
 
0.2%
4 38
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 23464
99.2%
2 56
 
0.2%
3 53
 
0.2%
5 43
 
0.2%
4 38
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23464
99.2%
2 56
 
0.2%
3 53
 
0.2%
5 43
 
0.2%
4 38
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23464
99.2%
2 56
 
0.2%
3 53
 
0.2%
5 43
 
0.2%
4 38
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23464
99.2%
2 56
 
0.2%
3 53
 
0.2%
5 43
 
0.2%
4 38
 
0.2%

POLY_170
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23514 
2
 
43
3
 
37
5
 
30
4
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23514
99.4%
2 43
 
0.2%
3 37
 
0.2%
5 30
 
0.1%
4 30
 
0.1%

Length

2025-04-17T05:07:19.897704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:19.945279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23514
99.4%
2 43
 
0.2%
3 37
 
0.2%
5 30
 
0.1%
4 30
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 23514
99.4%
2 43
 
0.2%
3 37
 
0.2%
5 30
 
0.1%
4 30
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23514
99.4%
2 43
 
0.2%
3 37
 
0.2%
5 30
 
0.1%
4 30
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23514
99.4%
2 43
 
0.2%
3 37
 
0.2%
5 30
 
0.1%
4 30
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23514
99.4%
2 43
 
0.2%
3 37
 
0.2%
5 30
 
0.1%
4 30
 
0.1%

POLY_180
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
23523 
2
 
39
4
 
34
5
 
29
3
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23523
99.4%
2 39
 
0.2%
4 34
 
0.1%
5 29
 
0.1%
3 29
 
0.1%

Length

2025-04-17T05:07:20.015370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:20.062578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23523
99.4%
2 39
 
0.2%
4 34
 
0.1%
5 29
 
0.1%
3 29
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 23523
99.4%
2 39
 
0.2%
4 34
 
0.1%
5 29
 
0.1%
3 29
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23523
99.4%
2 39
 
0.2%
4 34
 
0.1%
5 29
 
0.1%
3 29
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23523
99.4%
2 39
 
0.2%
4 34
 
0.1%
5 29
 
0.1%
3 29
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23523
99.4%
2 39
 
0.2%
4 34
 
0.1%
5 29
 
0.1%
3 29
 
0.1%

PH_010
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
10133 
3
7585 
1
2658 
4
2067 
5
1211 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row5
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 10133
42.8%
3 7585
32.1%
1 2658
 
11.2%
4 2067
 
8.7%
5 1211
 
5.1%

Length

2025-04-17T05:07:20.121049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:20.176395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 10133
42.8%
3 7585
32.1%
1 2658
 
11.2%
4 2067
 
8.7%
5 1211
 
5.1%

Most occurring characters

ValueCountFrequency (%)
2 10133
42.8%
3 7585
32.1%
1 2658
 
11.2%
4 2067
 
8.7%
5 1211
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 10133
42.8%
3 7585
32.1%
1 2658
 
11.2%
4 2067
 
8.7%
5 1211
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 10133
42.8%
3 7585
32.1%
1 2658
 
11.2%
4 2067
 
8.7%
5 1211
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 10133
42.8%
3 7585
32.1%
1 2658
 
11.2%
4 2067
 
8.7%
5 1211
 
5.1%

PH_020
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
14714 
3
5841 
5
 
1152
1
 
1083
2
 
864

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row4
4th row4
5th row2

Common Values

ValueCountFrequency (%)
4 14714
62.2%
3 5841
 
24.7%
5 1152
 
4.9%
1 1083
 
4.6%
2 864
 
3.7%

Length

2025-04-17T05:07:20.248883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:20.301935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 14714
62.2%
3 5841
 
24.7%
5 1152
 
4.9%
1 1083
 
4.6%
2 864
 
3.7%

Most occurring characters

ValueCountFrequency (%)
4 14714
62.2%
3 5841
 
24.7%
5 1152
 
4.9%
1 1083
 
4.6%
2 864
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 14714
62.2%
3 5841
 
24.7%
5 1152
 
4.9%
1 1083
 
4.6%
2 864
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 14714
62.2%
3 5841
 
24.7%
5 1152
 
4.9%
1 1083
 
4.6%
2 864
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 14714
62.2%
3 5841
 
24.7%
5 1152
 
4.9%
1 1083
 
4.6%
2 864
 
3.7%

PH_030
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
3
8158 
2
6313 
5
4062 
4
3264 
1
1857 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row3
4th row4
5th row3

Common Values

ValueCountFrequency (%)
3 8158
34.5%
2 6313
26.7%
5 4062
17.2%
4 3264
13.8%
1 1857
 
7.9%

Length

2025-04-17T05:07:20.371933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:20.425794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 8158
34.5%
2 6313
26.7%
5 4062
17.2%
4 3264
13.8%
1 1857
 
7.9%

Most occurring characters

ValueCountFrequency (%)
3 8158
34.5%
2 6313
26.7%
5 4062
17.2%
4 3264
13.8%
1 1857
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 8158
34.5%
2 6313
26.7%
5 4062
17.2%
4 3264
13.8%
1 1857
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 8158
34.5%
2 6313
26.7%
5 4062
17.2%
4 3264
13.8%
1 1857
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 8158
34.5%
2 6313
26.7%
5 4062
17.2%
4 3264
13.8%
1 1857
 
7.9%

PH_040
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
11921 
3
5627 
5
3959 
1
 
1081
2
 
1066

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row4
4th row4
5th row3

Common Values

ValueCountFrequency (%)
4 11921
50.4%
3 5627
23.8%
5 3959
 
16.7%
1 1081
 
4.6%
2 1066
 
4.5%

Length

2025-04-17T05:07:20.493497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:20.543449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 11921
50.4%
3 5627
23.8%
5 3959
 
16.7%
1 1081
 
4.6%
2 1066
 
4.5%

Most occurring characters

ValueCountFrequency (%)
4 11921
50.4%
3 5627
23.8%
5 3959
 
16.7%
1 1081
 
4.6%
2 1066
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 11921
50.4%
3 5627
23.8%
5 3959
 
16.7%
1 1081
 
4.6%
2 1066
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 11921
50.4%
3 5627
23.8%
5 3959
 
16.7%
1 1081
 
4.6%
2 1066
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 11921
50.4%
3 5627
23.8%
5 3959
 
16.7%
1 1081
 
4.6%
2 1066
 
4.5%

PH_051
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
8153 
3
7813 
4
3091 
1
2602 
5
1995 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row5
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
2 8153
34.5%
3 7813
33.0%
4 3091
 
13.1%
1 2602
 
11.0%
5 1995
 
8.4%

Length

2025-04-17T05:07:20.606679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:20.657004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 8153
34.5%
3 7813
33.0%
4 3091
 
13.1%
1 2602
 
11.0%
5 1995
 
8.4%

Most occurring characters

ValueCountFrequency (%)
2 8153
34.5%
3 7813
33.0%
4 3091
 
13.1%
1 2602
 
11.0%
5 1995
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 8153
34.5%
3 7813
33.0%
4 3091
 
13.1%
1 2602
 
11.0%
5 1995
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 8153
34.5%
3 7813
33.0%
4 3091
 
13.1%
1 2602
 
11.0%
5 1995
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 8153
34.5%
3 7813
33.0%
4 3091
 
13.1%
1 2602
 
11.0%
5 1995
 
8.4%

PH_061
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
12050 
3
6721 
5
1989 
2
1726 
1
 
1168

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 12050
50.9%
3 6721
28.4%
5 1989
 
8.4%
2 1726
 
7.3%
1 1168
 
4.9%

Length

2025-04-17T05:07:20.723404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:20.774862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 12050
50.9%
3 6721
28.4%
5 1989
 
8.4%
2 1726
 
7.3%
1 1168
 
4.9%

Most occurring characters

ValueCountFrequency (%)
4 12050
50.9%
3 6721
28.4%
5 1989
 
8.4%
2 1726
 
7.3%
1 1168
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 12050
50.9%
3 6721
28.4%
5 1989
 
8.4%
2 1726
 
7.3%
1 1168
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 12050
50.9%
3 6721
28.4%
5 1989
 
8.4%
2 1726
 
7.3%
1 1168
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 12050
50.9%
3 6721
28.4%
5 1989
 
8.4%
2 1726
 
7.3%
1 1168
 
4.9%

PH_052
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
9154 
1
5398 
3
4237 
5
2819 
4
2046 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row5
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
2 9154
38.7%
1 5398
22.8%
3 4237
17.9%
5 2819
 
11.9%
4 2046
 
8.6%

Length

2025-04-17T05:07:20.837335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:20.887911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 9154
38.7%
1 5398
22.8%
3 4237
17.9%
5 2819
 
11.9%
4 2046
 
8.6%

Most occurring characters

ValueCountFrequency (%)
2 9154
38.7%
1 5398
22.8%
3 4237
17.9%
5 2819
 
11.9%
4 2046
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 9154
38.7%
1 5398
22.8%
3 4237
17.9%
5 2819
 
11.9%
4 2046
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 9154
38.7%
1 5398
22.8%
3 4237
17.9%
5 2819
 
11.9%
4 2046
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 9154
38.7%
1 5398
22.8%
3 4237
17.9%
5 2819
 
11.9%
4 2046
 
8.6%

PH_062
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
3
7460 
2
5426 
4
5150 
5
2904 
1
2714 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row4
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 7460
31.5%
2 5426
22.9%
4 5150
21.8%
5 2904
 
12.3%
1 2714
 
11.5%

Length

2025-04-17T05:07:20.957404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:21.019751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 7460
31.5%
2 5426
22.9%
4 5150
21.8%
5 2904
 
12.3%
1 2714
 
11.5%

Most occurring characters

ValueCountFrequency (%)
3 7460
31.5%
2 5426
22.9%
4 5150
21.8%
5 2904
 
12.3%
1 2714
 
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 7460
31.5%
2 5426
22.9%
4 5150
21.8%
5 2904
 
12.3%
1 2714
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 7460
31.5%
2 5426
22.9%
4 5150
21.8%
5 2904
 
12.3%
1 2714
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 7460
31.5%
2 5426
22.9%
4 5150
21.8%
5 2904
 
12.3%
1 2714
 
11.5%

PH_110
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
9370 
1
8894 
3
2688 
4
1357 
5
1345 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 9370
39.6%
1 8894
37.6%
3 2688
 
11.4%
4 1357
 
5.7%
5 1345
 
5.7%

Length

2025-04-17T05:07:21.099425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:21.149264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 9370
39.6%
1 8894
37.6%
3 2688
 
11.4%
4 1357
 
5.7%
5 1345
 
5.7%

Most occurring characters

ValueCountFrequency (%)
2 9370
39.6%
1 8894
37.6%
3 2688
 
11.4%
4 1357
 
5.7%
5 1345
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 9370
39.6%
1 8894
37.6%
3 2688
 
11.4%
4 1357
 
5.7%
5 1345
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 9370
39.6%
1 8894
37.6%
3 2688
 
11.4%
4 1357
 
5.7%
5 1345
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 9370
39.6%
1 8894
37.6%
3 2688
 
11.4%
4 1357
 
5.7%
5 1345
 
5.7%

PH_120
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
8720 
3
8636 
2
3635 
5
1380 
1
1283 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row4
4th row2
5th row2

Common Values

ValueCountFrequency (%)
4 8720
36.9%
3 8636
36.5%
2 3635
15.4%
5 1380
 
5.8%
1 1283
 
5.4%

Length

2025-04-17T05:07:21.214364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:21.269459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 8720
36.9%
3 8636
36.5%
2 3635
15.4%
5 1380
 
5.8%
1 1283
 
5.4%

Most occurring characters

ValueCountFrequency (%)
4 8720
36.9%
3 8636
36.5%
2 3635
15.4%
5 1380
 
5.8%
1 1283
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 8720
36.9%
3 8636
36.5%
2 3635
15.4%
5 1380
 
5.8%
1 1283
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 8720
36.9%
3 8636
36.5%
2 3635
15.4%
5 1380
 
5.8%
1 1283
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 8720
36.9%
3 8636
36.5%
2 3635
15.4%
5 1380
 
5.8%
1 1283
 
5.4%

PH_070
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
3
7272 
2
7097 
1
3678 
4
3615 
5
1992 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row5
3rd row1
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 7272
30.7%
2 7097
30.0%
1 3678
15.5%
4 3615
15.3%
5 1992
 
8.4%

Length

2025-04-17T05:07:21.337567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:21.403354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 7272
30.7%
2 7097
30.0%
1 3678
15.5%
4 3615
15.3%
5 1992
 
8.4%

Most occurring characters

ValueCountFrequency (%)
3 7272
30.7%
2 7097
30.0%
1 3678
15.5%
4 3615
15.3%
5 1992
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 7272
30.7%
2 7097
30.0%
1 3678
15.5%
4 3615
15.3%
5 1992
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 7272
30.7%
2 7097
30.0%
1 3678
15.5%
4 3615
15.3%
5 1992
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 7272
30.7%
2 7097
30.0%
1 3678
15.5%
4 3615
15.3%
5 1992
 
8.4%

PH_080
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
12202 
3
5727 
2
2120 
5
2060 
1
1545 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 12202
51.6%
3 5727
24.2%
2 2120
 
9.0%
5 2060
 
8.7%
1 1545
 
6.5%

Length

2025-04-17T05:07:21.490837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:21.580564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 12202
51.6%
3 5727
24.2%
2 2120
 
9.0%
5 2060
 
8.7%
1 1545
 
6.5%

Most occurring characters

ValueCountFrequency (%)
4 12202
51.6%
3 5727
24.2%
2 2120
 
9.0%
5 2060
 
8.7%
1 1545
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 12202
51.6%
3 5727
24.2%
2 2120
 
9.0%
5 2060
 
8.7%
1 1545
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 12202
51.6%
3 5727
24.2%
2 2120
 
9.0%
5 2060
 
8.7%
1 1545
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 12202
51.6%
3 5727
24.2%
2 2120
 
9.0%
5 2060
 
8.7%
1 1545
 
6.5%

PH_130
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
3
6838 
2
6245 
4
4134 
1
3402 
5
3035 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row5
3rd row1
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 6838
28.9%
2 6245
26.4%
4 4134
17.5%
1 3402
14.4%
5 3035
12.8%

Length

2025-04-17T05:07:21.684925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:21.755713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 6838
28.9%
2 6245
26.4%
4 4134
17.5%
1 3402
14.4%
5 3035
12.8%

Most occurring characters

ValueCountFrequency (%)
3 6838
28.9%
2 6245
26.4%
4 4134
17.5%
1 3402
14.4%
5 3035
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 6838
28.9%
2 6245
26.4%
4 4134
17.5%
1 3402
14.4%
5 3035
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 6838
28.9%
2 6245
26.4%
4 4134
17.5%
1 3402
14.4%
5 3035
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 6838
28.9%
2 6245
26.4%
4 4134
17.5%
1 3402
14.4%
5 3035
12.8%

PH_140
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
10959 
3
5587 
5
3046 
2
2374 
1
1688 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 10959
46.3%
3 5587
23.6%
5 3046
 
12.9%
2 2374
 
10.0%
1 1688
 
7.1%

Length

2025-04-17T05:07:21.853174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:21.918321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 10959
46.3%
3 5587
23.6%
5 3046
 
12.9%
2 2374
 
10.0%
1 1688
 
7.1%

Most occurring characters

ValueCountFrequency (%)
4 10959
46.3%
3 5587
23.6%
5 3046
 
12.9%
2 2374
 
10.0%
1 1688
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 10959
46.3%
3 5587
23.6%
5 3046
 
12.9%
2 2374
 
10.0%
1 1688
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 10959
46.3%
3 5587
23.6%
5 3046
 
12.9%
2 2374
 
10.0%
1 1688
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 10959
46.3%
3 5587
23.6%
5 3046
 
12.9%
2 2374
 
10.0%
1 1688
 
7.1%

PH_090
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
8169 
3
8148 
5
2959 
2
2921 
1
1457 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row3
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 8169
34.5%
3 8148
34.4%
5 2959
 
12.5%
2 2921
 
12.3%
1 1457
 
6.2%

Length

2025-04-17T05:07:22.001896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:22.071911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 8169
34.5%
3 8148
34.4%
5 2959
 
12.5%
2 2921
 
12.3%
1 1457
 
6.2%

Most occurring characters

ValueCountFrequency (%)
4 8169
34.5%
3 8148
34.4%
5 2959
 
12.5%
2 2921
 
12.3%
1 1457
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 8169
34.5%
3 8148
34.4%
5 2959
 
12.5%
2 2921
 
12.3%
1 1457
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 8169
34.5%
3 8148
34.4%
5 2959
 
12.5%
2 2921
 
12.3%
1 1457
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 8169
34.5%
3 8148
34.4%
5 2959
 
12.5%
2 2921
 
12.3%
1 1457
 
6.2%

PH_100
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
16169 
5
3040 
3
2668 
1
 
1096
2
 
681

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 16169
68.4%
5 3040
 
12.9%
3 2668
 
11.3%
1 1096
 
4.6%
2 681
 
2.9%

Length

2025-04-17T05:07:22.159741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:22.223160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 16169
68.4%
5 3040
 
12.9%
3 2668
 
11.3%
1 1096
 
4.6%
2 681
 
2.9%

Most occurring characters

ValueCountFrequency (%)
4 16169
68.4%
5 3040
 
12.9%
3 2668
 
11.3%
1 1096
 
4.6%
2 681
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 16169
68.4%
5 3040
 
12.9%
3 2668
 
11.3%
1 1096
 
4.6%
2 681
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 16169
68.4%
5 3040
 
12.9%
3 2668
 
11.3%
1 1096
 
4.6%
2 681
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 16169
68.4%
5 3040
 
12.9%
3 2668
 
11.3%
1 1096
 
4.6%
2 681
 
2.9%

CA_020
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
3
7097 
4
6687 
5
3757 
2
3303 
1
2810 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row3
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3 7097
30.0%
4 6687
28.3%
5 3757
15.9%
2 3303
14.0%
1 2810
 
11.9%

Length

2025-04-17T05:07:22.330443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:22.416319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 7097
30.0%
4 6687
28.3%
5 3757
15.9%
2 3303
14.0%
1 2810
 
11.9%

Most occurring characters

ValueCountFrequency (%)
3 7097
30.0%
4 6687
28.3%
5 3757
15.9%
2 3303
14.0%
1 2810
 
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 7097
30.0%
4 6687
28.3%
5 3757
15.9%
2 3303
14.0%
1 2810
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 7097
30.0%
4 6687
28.3%
5 3757
15.9%
2 3303
14.0%
1 2810
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 7097
30.0%
4 6687
28.3%
5 3757
15.9%
2 3303
14.0%
1 2810
 
11.9%

ELC_041
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
7281 
3
5916 
5
4540 
1
3001 
2
2916 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
4 7281
30.8%
3 5916
25.0%
5 4540
19.2%
1 3001
12.7%
2 2916
12.3%

Length

2025-04-17T05:07:22.521563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:22.601659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 7281
30.8%
3 5916
25.0%
5 4540
19.2%
1 3001
12.7%
2 2916
12.3%

Most occurring characters

ValueCountFrequency (%)
4 7281
30.8%
3 5916
25.0%
5 4540
19.2%
1 3001
12.7%
2 2916
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 7281
30.8%
3 5916
25.0%
5 4540
19.2%
1 3001
12.7%
2 2916
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 7281
30.8%
3 5916
25.0%
5 4540
19.2%
1 3001
12.7%
2 2916
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 7281
30.8%
3 5916
25.0%
5 4540
19.2%
1 3001
12.7%
2 2916
12.3%

ELC_042
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
6732 
3
6170 
5
4845 
2
3080 
1
2827 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
4 6732
28.5%
3 6170
26.1%
5 4845
20.5%
2 3080
13.0%
1 2827
12.0%

Length

2025-04-17T05:07:22.714707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:22.801826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 6732
28.5%
3 6170
26.1%
5 4845
20.5%
2 3080
13.0%
1 2827
12.0%

Most occurring characters

ValueCountFrequency (%)
4 6732
28.5%
3 6170
26.1%
5 4845
20.5%
2 3080
13.0%
1 2827
12.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 6732
28.5%
3 6170
26.1%
5 4845
20.5%
2 3080
13.0%
1 2827
12.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 6732
28.5%
3 6170
26.1%
5 4845
20.5%
2 3080
13.0%
1 2827
12.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 6732
28.5%
3 6170
26.1%
5 4845
20.5%
2 3080
13.0%
1 2827
12.0%

ALC_080
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
10412 
3
5219 
5
3319 
1
2409 
2
2295 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row4
4th row1
5th row4

Common Values

ValueCountFrequency (%)
4 10412
44.0%
3 5219
22.1%
5 3319
 
14.0%
1 2409
 
10.2%
2 2295
 
9.7%

Length

2025-04-17T05:07:22.877203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:22.932885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 10412
44.0%
3 5219
22.1%
5 3319
 
14.0%
1 2409
 
10.2%
2 2295
 
9.7%

Most occurring characters

ValueCountFrequency (%)
4 10412
44.0%
3 5219
22.1%
5 3319
 
14.0%
1 2409
 
10.2%
2 2295
 
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 10412
44.0%
3 5219
22.1%
5 3319
 
14.0%
1 2409
 
10.2%
2 2295
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 10412
44.0%
3 5219
22.1%
5 3319
 
14.0%
1 2409
 
10.2%
2 2295
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 10412
44.0%
3 5219
22.1%
5 3319
 
14.0%
1 2409
 
10.2%
2 2295
 
9.7%

CAN_050
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
5259 
3
4750 
5
4652 
4
4503 
2
4490 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row4
4th row1
5th row4

Common Values

ValueCountFrequency (%)
1 5259
22.2%
3 4750
20.1%
5 4652
19.7%
4 4503
19.0%
2 4490
19.0%

Length

2025-04-17T05:07:23.007964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:23.068812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 5259
22.2%
3 4750
20.1%
5 4652
19.7%
4 4503
19.0%
2 4490
19.0%

Most occurring characters

ValueCountFrequency (%)
1 5259
22.2%
3 4750
20.1%
5 4652
19.7%
4 4503
19.0%
2 4490
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5259
22.2%
3 4750
20.1%
5 4652
19.7%
4 4503
19.0%
2 4490
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5259
22.2%
3 4750
20.1%
5 4652
19.7%
4 4503
19.0%
2 4490
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5259
22.2%
3 4750
20.1%
5 4652
19.7%
4 4503
19.0%
2 4490
19.0%

MET_030
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
10862 
5
6336 
2
4555 
3
1316 
4
 
585

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 10862
45.9%
5 6336
26.8%
2 4555
19.3%
3 1316
 
5.6%
4 585
 
2.5%

Length

2025-04-17T05:07:23.147299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:23.206360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 10862
45.9%
5 6336
26.8%
2 4555
19.3%
3 1316
 
5.6%
4 585
 
2.5%

Most occurring characters

ValueCountFrequency (%)
1 10862
45.9%
5 6336
26.8%
2 4555
19.3%
3 1316
 
5.6%
4 585
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 10862
45.9%
5 6336
26.8%
2 4555
19.3%
3 1316
 
5.6%
4 585
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 10862
45.9%
5 6336
26.8%
2 4555
19.3%
3 1316
 
5.6%
4 585
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 10862
45.9%
5 6336
26.8%
2 4555
19.3%
3 1316
 
5.6%
4 585
 
2.5%

XTC_030
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
10223 
5
7453 
2
4140 
3
1255 
4
 
583

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row3
4th row2
5th row5

Common Values

ValueCountFrequency (%)
1 10223
43.2%
5 7453
31.5%
2 4140
17.5%
3 1255
 
5.3%
4 583
 
2.5%

Length

2025-04-17T05:07:23.276333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:23.330986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 10223
43.2%
5 7453
31.5%
2 4140
17.5%
3 1255
 
5.3%
4 583
 
2.5%

Most occurring characters

ValueCountFrequency (%)
1 10223
43.2%
5 7453
31.5%
2 4140
17.5%
3 1255
 
5.3%
4 583
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 10223
43.2%
5 7453
31.5%
2 4140
17.5%
3 1255
 
5.3%
4 583
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 10223
43.2%
5 7453
31.5%
2 4140
17.5%
3 1255
 
5.3%
4 583
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 10223
43.2%
5 7453
31.5%
2 4140
17.5%
3 1255
 
5.3%
4 583
 
2.5%

HAL_030
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
9452 
5
6717 
2
4333 
3
2184 
4
968 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row4
4th row3
5th row5

Common Values

ValueCountFrequency (%)
1 9452
40.0%
5 6717
28.4%
2 4333
18.3%
3 2184
 
9.2%
4 968
 
4.1%

Length

2025-04-17T05:07:23.406748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:23.458043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 9452
40.0%
5 6717
28.4%
2 4333
18.3%
3 2184
 
9.2%
4 968
 
4.1%

Most occurring characters

ValueCountFrequency (%)
1 9452
40.0%
5 6717
28.4%
2 4333
18.3%
3 2184
 
9.2%
4 968
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 9452
40.0%
5 6717
28.4%
2 4333
18.3%
3 2184
 
9.2%
4 968
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 9452
40.0%
5 6717
28.4%
2 4333
18.3%
3 2184
 
9.2%
4 968
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 9452
40.0%
5 6717
28.4%
2 4333
18.3%
3 2184
 
9.2%
4 968
 
4.1%

COC_030
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
10919 
5
5921 
2
4198 
3
1785 
4
 
831

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10919
46.2%
5 5921
25.0%
2 4198
 
17.7%
3 1785
 
7.5%
4 831
 
3.5%

Length

2025-04-17T05:07:24.463548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:24.516776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 10919
46.2%
5 5921
25.0%
2 4198
 
17.7%
3 1785
 
7.5%
4 831
 
3.5%

Most occurring characters

ValueCountFrequency (%)
1 10919
46.2%
5 5921
25.0%
2 4198
 
17.7%
3 1785
 
7.5%
4 831
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 10919
46.2%
5 5921
25.0%
2 4198
 
17.7%
3 1785
 
7.5%
4 831
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 10919
46.2%
5 5921
25.0%
2 4198
 
17.7%
3 1785
 
7.5%
4 831
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 10919
46.2%
5 5921
25.0%
2 4198
 
17.7%
3 1785
 
7.5%
4 831
 
3.5%

PR_090
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
6946 
5
5817 
2
4869 
3
3833 
4
2189 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row3
4th row4
5th row1

Common Values

ValueCountFrequency (%)
1 6946
29.4%
5 5817
24.6%
2 4869
20.6%
3 3833
16.2%
4 2189
 
9.3%

Length

2025-04-17T05:07:24.586133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:24.639388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 6946
29.4%
5 5817
24.6%
2 4869
20.6%
3 3833
16.2%
4 2189
 
9.3%

Most occurring characters

ValueCountFrequency (%)
1 6946
29.4%
5 5817
24.6%
2 4869
20.6%
3 3833
16.2%
4 2189
 
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6946
29.4%
5 5817
24.6%
2 4869
20.6%
3 3833
16.2%
4 2189
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6946
29.4%
5 5817
24.6%
2 4869
20.6%
3 3833
16.2%
4 2189
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6946
29.4%
5 5817
24.6%
2 4869
20.6%
3 3833
16.2%
4 2189
 
9.3%

STI_070
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
5973 
5
5849 
2
4393 
3
4322 
4
3117 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 5973
25.3%
5 5849
24.7%
2 4393
18.6%
3 4322
18.3%
4 3117
13.2%

Length

2025-04-17T05:07:24.710882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:24.766805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 5973
25.3%
5 5849
24.7%
2 4393
18.6%
3 4322
18.3%
4 3117
13.2%

Most occurring characters

ValueCountFrequency (%)
1 5973
25.3%
5 5849
24.7%
2 4393
18.6%
3 4322
18.3%
4 3117
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5973
25.3%
5 5849
24.7%
2 4393
18.6%
3 4322
18.3%
4 3117
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5973
25.3%
5 5849
24.7%
2 4393
18.6%
3 4322
18.3%
4 3117
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5973
25.3%
5 5849
24.7%
2 4393
18.6%
3 4322
18.3%
4 3117
13.2%

DR_010
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
22713 
3
 
640
2
 
301

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row3

Common Values

ValueCountFrequency (%)
1 22713
96.0%
3 640
 
2.7%
2 301
 
1.3%

Length

2025-04-17T05:07:24.838583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:24.885090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 22713
96.0%
3 640
 
2.7%
2 301
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 22713
96.0%
3 640
 
2.7%
2 301
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 22713
96.0%
3 640
 
2.7%
2 301
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 22713
96.0%
3 640
 
2.7%
2 301
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 22713
96.0%
3 640
 
2.7%
2 301
 
1.3%

DR_020
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
22915 
3
 
389
2
 
350

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 22915
96.9%
3 389
 
1.6%
2 350
 
1.5%

Length

2025-04-17T05:07:24.939308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:24.984645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 22915
96.9%
3 389
 
1.6%
2 350
 
1.5%

Most occurring characters

ValueCountFrequency (%)
1 22915
96.9%
3 389
 
1.6%
2 350
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 22915
96.9%
3 389
 
1.6%
2 350
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 22915
96.9%
3 389
 
1.6%
2 350
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 22915
96.9%
3 389
 
1.6%
2 350
 
1.5%

DR_060
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
15070 
3
4640 
4
2054 
2
1890 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
1 15070
63.7%
3 4640
 
19.6%
4 2054
 
8.7%
2 1890
 
8.0%

Length

2025-04-17T05:07:25.046616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:25.101373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 15070
63.7%
3 4640
 
19.6%
4 2054
 
8.7%
2 1890
 
8.0%

Most occurring characters

ValueCountFrequency (%)
1 15070
63.7%
3 4640
 
19.6%
4 2054
 
8.7%
2 1890
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 15070
63.7%
3 4640
 
19.6%
4 2054
 
8.7%
2 1890
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 15070
63.7%
3 4640
 
19.6%
4 2054
 
8.7%
2 1890
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 15070
63.7%
3 4640
 
19.6%
4 2054
 
8.7%
2 1890
 
8.0%

DR_070
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
18900 
4
 
1747
3
 
1727
2
 
1280

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 18900
79.9%
4 1747
 
7.4%
3 1727
 
7.3%
2 1280
 
5.4%

Length

2025-04-17T05:07:25.172282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:25.220435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 18900
79.9%
4 1747
 
7.4%
3 1727
 
7.3%
2 1280
 
5.4%

Most occurring characters

ValueCountFrequency (%)
1 18900
79.9%
4 1747
 
7.4%
3 1727
 
7.3%
2 1280
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 18900
79.9%
4 1747
 
7.4%
3 1727
 
7.3%
2 1280
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 18900
79.9%
4 1747
 
7.4%
3 1727
 
7.3%
2 1280
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 18900
79.9%
4 1747
 
7.4%
3 1727
 
7.3%
2 1280
 
5.4%

BEH_010
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
18511 
3
4415 
1
 
659
2
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 18511
78.3%
3 4415
 
18.7%
1 659
 
2.8%
2 69
 
0.3%

Length

2025-04-17T05:07:25.278800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:25.329219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 18511
78.3%
3 4415
 
18.7%
1 659
 
2.8%
2 69
 
0.3%

Most occurring characters

ValueCountFrequency (%)
4 18511
78.3%
3 4415
 
18.7%
1 659
 
2.8%
2 69
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 18511
78.3%
3 4415
 
18.7%
1 659
 
2.8%
2 69
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 18511
78.3%
3 4415
 
18.7%
1 659
 
2.8%
2 69
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 18511
78.3%
3 4415
 
18.7%
1 659
 
2.8%
2 69
 
0.3%

BEH_020
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
19873 
3
2887 
1
 
694
2
 
200

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 19873
84.0%
3 2887
 
12.2%
1 694
 
2.9%
2 200
 
0.8%

Length

2025-04-17T05:07:25.400003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:25.456425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 19873
84.0%
3 2887
 
12.2%
1 694
 
2.9%
2 200
 
0.8%

Most occurring characters

ValueCountFrequency (%)
4 19873
84.0%
3 2887
 
12.2%
1 694
 
2.9%
2 200
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 19873
84.0%
3 2887
 
12.2%
1 694
 
2.9%
2 200
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 19873
84.0%
3 2887
 
12.2%
1 694
 
2.9%
2 200
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 19873
84.0%
3 2887
 
12.2%
1 694
 
2.9%
2 200
 
0.8%

BEH_030
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
18874 
3
2774 
1
 
1881
2
 
125

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 18874
79.8%
3 2774
 
11.7%
1 1881
 
8.0%
2 125
 
0.5%

Length

2025-04-17T05:07:25.520412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:25.566325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 18874
79.8%
3 2774
 
11.7%
1 1881
 
8.0%
2 125
 
0.5%

Most occurring characters

ValueCountFrequency (%)
4 18874
79.8%
3 2774
 
11.7%
1 1881
 
8.0%
2 125
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 18874
79.8%
3 2774
 
11.7%
1 1881
 
8.0%
2 125
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 18874
79.8%
3 2774
 
11.7%
1 1881
 
8.0%
2 125
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 18874
79.8%
3 2774
 
11.7%
1 1881
 
8.0%
2 125
 
0.5%

BEH_040
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
20216 
3
2291 
1
 
989
2
 
158

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 20216
85.5%
3 2291
 
9.7%
1 989
 
4.2%
2 158
 
0.7%

Length

2025-04-17T05:07:25.632450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:25.687706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 20216
85.5%
3 2291
 
9.7%
1 989
 
4.2%
2 158
 
0.7%

Most occurring characters

ValueCountFrequency (%)
4 20216
85.5%
3 2291
 
9.7%
1 989
 
4.2%
2 158
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 20216
85.5%
3 2291
 
9.7%
1 989
 
4.2%
2 158
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 20216
85.5%
3 2291
 
9.7%
1 989
 
4.2%
2 158
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 20216
85.5%
3 2291
 
9.7%
1 989
 
4.2%
2 158
 
0.7%

BUL_010
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
22650 
1
 
1004

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 22650
95.8%
1 1004
 
4.2%

Length

2025-04-17T05:07:25.752625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:25.797296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 22650
95.8%
1 1004
 
4.2%

Most occurring characters

ValueCountFrequency (%)
2 22650
95.8%
1 1004
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 22650
95.8%
1 1004
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 22650
95.8%
1 1004
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 22650
95.8%
1 1004
 
4.2%

BUL_020
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
19008 
1
4646 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 19008
80.4%
1 4646
 
19.6%

Length

2025-04-17T05:07:25.850543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:25.894915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 19008
80.4%
1 4646
 
19.6%

Most occurring characters

ValueCountFrequency (%)
2 19008
80.4%
1 4646
 
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 19008
80.4%
1 4646
 
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 19008
80.4%
1 4646
 
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 19008
80.4%
1 4646
 
19.6%

BUL_030
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
18289 
1
5365 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 18289
77.3%
1 5365
 
22.7%

Length

2025-04-17T05:07:25.954628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:25.997789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 18289
77.3%
1 5365
 
22.7%

Most occurring characters

ValueCountFrequency (%)
2 18289
77.3%
1 5365
 
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 18289
77.3%
1 5365
 
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 18289
77.3%
1 5365
 
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 18289
77.3%
1 5365
 
22.7%

BUL_040
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
21461 
1
2193 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 21461
90.7%
1 2193
 
9.3%

Length

2025-04-17T05:07:26.054347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:26.098329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 21461
90.7%
1 2193
 
9.3%

Most occurring characters

ValueCountFrequency (%)
2 21461
90.7%
1 2193
 
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 21461
90.7%
1 2193
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 21461
90.7%
1 2193
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 21461
90.7%
1 2193
 
9.3%

BUL_050
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
21903 
1
 
1751

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 21903
92.6%
1 1751
 
7.4%

Length

2025-04-17T05:07:26.160760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:26.204471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 21903
92.6%
1 1751
 
7.4%

Most occurring characters

ValueCountFrequency (%)
2 21903
92.6%
1 1751
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 21903
92.6%
1 1751
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 21903
92.6%
1 1751
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 21903
92.6%
1 1751
 
7.4%

BUL_060
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
18723 
2
2425 
3
 
1046
4
 
762
5
 
698

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 18723
79.2%
2 2425
 
10.3%
3 1046
 
4.4%
4 762
 
3.2%
5 698
 
3.0%

Length

2025-04-17T05:07:26.264304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:26.318938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 18723
79.2%
2 2425
 
10.3%
3 1046
 
4.4%
4 762
 
3.2%
5 698
 
3.0%

Most occurring characters

ValueCountFrequency (%)
1 18723
79.2%
2 2425
 
10.3%
3 1046
 
4.4%
4 762
 
3.2%
5 698
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 18723
79.2%
2 2425
 
10.3%
3 1046
 
4.4%
4 762
 
3.2%
5 698
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 18723
79.2%
2 2425
 
10.3%
3 1046
 
4.4%
4 762
 
3.2%
5 698
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 18723
79.2%
2 2425
 
10.3%
3 1046
 
4.4%
4 762
 
3.2%
5 698
 
3.0%

BUL_070
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
23106 
1
 
548

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 23106
97.7%
1 548
 
2.3%

Length

2025-04-17T05:07:26.395571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:26.439232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 23106
97.7%
1 548
 
2.3%

Most occurring characters

ValueCountFrequency (%)
2 23106
97.7%
1 548
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 23106
97.7%
1 548
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 23106
97.7%
1 548
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 23106
97.7%
1 548
 
2.3%

BUL_080
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
21805 
1
 
1849

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 21805
92.2%
1 1849
 
7.8%

Length

2025-04-17T05:07:26.491458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:26.535930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 21805
92.2%
1 1849
 
7.8%

Most occurring characters

ValueCountFrequency (%)
2 21805
92.2%
1 1849
 
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 21805
92.2%
1 1849
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 21805
92.2%
1 1849
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 21805
92.2%
1 1849
 
7.8%

BUL_090
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
21251 
1
2403 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 21251
89.8%
1 2403
 
10.2%

Length

2025-04-17T05:07:26.586059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:26.627408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 21251
89.8%
1 2403
 
10.2%

Most occurring characters

ValueCountFrequency (%)
2 21251
89.8%
1 2403
 
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 21251
89.8%
1 2403
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 21251
89.8%
1 2403
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 21251
89.8%
1 2403
 
10.2%

BUL_100
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
22993 
1
 
661

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 22993
97.2%
1 661
 
2.8%

Length

2025-04-17T05:07:26.679508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:26.718935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 22993
97.2%
1 661
 
2.8%

Most occurring characters

ValueCountFrequency (%)
2 22993
97.2%
1 661
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 22993
97.2%
1 661
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 22993
97.2%
1 661
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 22993
97.2%
1 661
 
2.8%

BUL_110
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
23363 
1
 
291

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 23363
98.8%
1 291
 
1.2%

Length

2025-04-17T05:07:26.768106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:26.809661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 23363
98.8%
1 291
 
1.2%

Most occurring characters

ValueCountFrequency (%)
2 23363
98.8%
1 291
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 23363
98.8%
1 291
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 23363
98.8%
1 291
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 23363
98.8%
1 291
 
1.2%

BUL_120
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
21327 
2
 
1536
3
 
362
5
 
233
4
 
196

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 21327
90.2%
2 1536
 
6.5%
3 362
 
1.5%
5 233
 
1.0%
4 196
 
0.8%

Length

2025-04-17T05:07:26.861270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:26.913916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 21327
90.2%
2 1536
 
6.5%
3 362
 
1.5%
5 233
 
1.0%
4 196
 
0.8%

Most occurring characters

ValueCountFrequency (%)
1 21327
90.2%
2 1536
 
6.5%
3 362
 
1.5%
5 233
 
1.0%
4 196
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 21327
90.2%
2 1536
 
6.5%
3 362
 
1.5%
5 233
 
1.0%
4 196
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 21327
90.2%
2 1536
 
6.5%
3 362
 
1.5%
5 233
 
1.0%
4 196
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 21327
90.2%
2 1536
 
6.5%
3 362
 
1.5%
5 233
 
1.0%
4 196
 
0.8%

DVTY1ST
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
3
23320 
1
 
267
2
 
67

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 23320
98.6%
1 267
 
1.1%
2 67
 
0.3%

Length

2025-04-17T05:07:26.979925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:27.025542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 23320
98.6%
1 267
 
1.1%
2 67
 
0.3%

Most occurring characters

ValueCountFrequency (%)
3 23320
98.6%
1 267
 
1.1%
2 67
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 23320
98.6%
1 267
 
1.1%
2 67
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 23320
98.6%
1 267
 
1.1%
2 67
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 23320
98.6%
1 267
 
1.1%
2 67
 
0.3%

DVTY2ST
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.761182
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size369.6 KiB
2025-04-17T05:07:27.069659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q17
median7
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.79735318
Coefficient of variation (CV)0.11793103
Kurtosis23.354188
Mean6.761182
Median Absolute Deviation (MAD)0
Skewness-4.5140133
Sum159929
Variance0.63577209
MonotonicityNot monotonic
2025-04-17T05:07:27.126749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 20706
87.5%
6 1669
 
7.1%
5 584
 
2.5%
4 361
 
1.5%
2 141
 
0.6%
1 126
 
0.5%
3 67
 
0.3%
ValueCountFrequency (%)
1 126
 
0.5%
2 141
 
0.6%
3 67
 
0.3%
4 361
 
1.5%
5 584
 
2.5%
6 1669
 
7.1%
7 20706
87.5%
ValueCountFrequency (%)
7 20706
87.5%
6 1669
 
7.1%
5 584
 
2.5%
4 361
 
1.5%
3 67
 
0.3%
2 141
 
0.6%
1 126
 
0.5%

DVLAST30
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
23026 
1
 
628

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23654
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 23026
97.3%
1 628
 
2.7%

Length

2025-04-17T05:07:27.208486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T05:07:27.252789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 23026
97.3%
1 628
 
2.7%

Most occurring characters

ValueCountFrequency (%)
2 23026
97.3%
1 628
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 23026
97.3%
1 628
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 23026
97.3%
1 628
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 23026
97.3%
1 628
 
2.7%

Interactions

2025-04-17T05:07:05.418542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:40.743961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-17T05:06:46.670382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:48.350152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:50.619184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:51.947541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:53.302700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:55.017556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-17T05:07:06.734012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:41.518819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:43.091271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:44.355317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:46.005103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:47.433774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:49.977775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:51.322787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:52.659384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:54.397695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:55.773838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:57.113914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:58.433576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:00.612398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:01.930830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:03.298608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:04.742374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:06.805497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:41.602468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:43.168293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:44.427935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:46.092031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:47.553455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:50.055412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:51.405882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:52.739552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:54.480659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:55.852592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:57.194109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:58.515141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:00.686298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:02.007321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:03.382417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:04.823653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:06.883097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:41.685199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:43.235232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:44.501538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:46.170323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:47.660224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:50.133097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:51.482083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:52.814508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:54.557514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:55.927246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:57.273469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:58.588033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:00.760414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:02.080840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:03.462967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:04.904796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:06.960649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:41.770472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:43.305912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:44.575917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:46.257004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:47.762130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:50.209574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:51.554942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:52.887444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:54.634394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:56.004628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:57.354073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:58.662932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:00.829037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:02.154044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:03.545134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:04.987533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:07.038543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:41.855210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:43.377144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:44.650499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:46.344613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:47.861130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:50.285915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:51.634017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:52.970311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:54.705019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:56.076190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:57.431836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:58.742771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:00.901885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:02.236399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:03.633006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:05.065347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:07.113879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:41.940661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:43.448236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:44.726347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:46.424733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:47.964205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:50.376947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:51.705711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:53.051718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:54.782320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:56.149480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:57.515741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:58.863111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:00.977061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:02.312430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:03.712884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:05.145185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:07.201645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:42.026496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:43.523408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:44.804966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:46.510297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:48.075980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:50.458115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:51.787117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:53.132482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:54.864588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:56.227682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:57.593702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:58.981830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:01.060316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:02.401483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:03.796140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:05.230482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:07.288869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:42.332946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:43.598457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:45.150690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:46.592579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:48.213148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:50.547032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:51.868411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:53.217531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:54.946591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:56.313817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:57.672940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:06:59.092146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:01.140032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:02.479437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:03.879138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T05:07:05.328569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-17T05:07:27.461564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ALC_010ALC_080BEH_010BEH_020BEH_030BEH_040BS_010BUL_010BUL_020BUL_030BUL_040BUL_050BUL_060BUL_070BUL_080BUL_090BUL_100BUL_110BUL_120BZP_010CAN_010CAN_050CAN_130CAN_140CA_020CI_010COC_010COC_030DEX_010DR_010DR_020DR_060DR_070DVDESCRIBEDVGENDERDVLAST30DVORIENTDVRESDVTY1STDVTY2STDVURBANELC_026aELC_026bELC_026cELC_041ELC_042GH_010GH_020GLU_010GRADEGRV_010HAL_010HAL_030HER_010MET_010MET_030NRG_010NRG_020NRG_030NRG_040NRG_050PH_010PH_020PH_030PH_040PH_051PH_052PH_061PH_062PH_070PH_080PH_090PH_100PH_110PH_120PH_130PH_140POLY_010POLY_020POLY_030POLY_040POLY_050POLY_060POLY_070POLY_080POLY_090POLY_100POLY_110POLY_120POLY_130POLY_140POLY_150POLY_160POLY_170POLY_180PROVIDPR_030PR_050PR_060PR_090PR_100PR_110SAL_010SED_030SED_050SEQIDSLP_010SS_010STI_030STI_050STI_070STI_080SYN_010TNB_010TP_016TP_046TP_056TP_066TP_086TRP_010TS_011UND_010UND_020VAP_010VAP_020VAP_030VAP_040VAP_050aVAP_050bVAP_060WTPUMFXTC_010XTC_030
ALC_0101.0000.3760.0590.1600.1670.1580.0480.0190.0710.0830.0930.0400.0640.0630.1110.1260.0860.0370.1250.0340.4560.3720.3860.3830.2980.5350.0980.1760.1300.2030.1850.2880.2860.2180.0500.1680.0830.0970.1250.3530.0100.5130.3700.3190.4010.3370.0640.2180.0970.3820.1230.1890.2530.0430.0650.1820.3420.3600.2620.2520.4620.1550.1080.1690.1510.2510.2450.2110.2400.2610.2340.0910.0930.2290.1740.2380.2370.0600.0750.1300.0380.0780.0990.0780.0810.0950.0810.0670.0750.0470.0600.0580.0650.0660.0650.0690.0830.0640.1160.1520.1370.1490.0520.0920.1051.0000.1620.3520.1370.1160.1660.0790.1630.0310.2490.1750.2750.1420.1300.0510.3710.1540.1450.5170.4400.5260.4160.4180.4150.4950.0240.0970.212
ALC_0800.3761.0000.0300.0470.0480.0420.0200.0190.0690.0900.0530.0320.0480.0250.0900.1040.0470.0230.0530.0080.2420.5120.1270.1070.5550.1370.0370.3740.0480.0870.0780.1560.1190.0870.0750.0840.0600.0890.0380.0820.0040.1340.0970.0890.5260.5030.0480.1020.0320.1580.0400.0730.3590.0140.0180.3600.1700.1660.1280.1150.2540.1980.2110.1590.1660.2100.1920.2160.1860.2090.2090.1900.1940.2260.2290.1850.1900.0090.0230.0380.0060.0230.0260.0220.0220.0230.0240.0190.0130.0100.0180.0130.0170.0170.0160.0350.0340.0220.0400.3770.0600.0400.0150.0360.0491.0000.0590.1640.0600.0400.3830.0330.0660.0080.0680.0470.0710.0370.0310.0170.1240.0660.0590.1720.1120.1340.1060.1100.1090.1250.0190.0380.330
BEH_0100.0590.0301.0000.4840.5360.4580.0480.0550.0460.0450.0470.0590.0210.0590.0440.0400.0530.0630.0340.0600.0560.0310.0350.0490.0550.0780.0620.0310.0380.0440.0390.0470.0630.0750.0190.0970.0540.0190.0820.0860.0540.0430.0430.0310.0330.0300.0710.0380.0390.0170.0300.0500.0310.0660.0560.0320.0790.0660.0650.0510.0310.0610.0720.0420.0530.0450.0570.0600.0660.0470.0540.0440.0440.0330.0400.0340.0450.0620.0540.0450.0520.0500.0280.0470.0460.0510.0400.0330.0350.0510.0600.0670.0360.0460.0430.0430.0430.0440.0340.0400.0280.0430.0560.0310.0351.0000.0400.1000.0360.0320.0300.0480.0510.0610.0460.0350.0270.0440.0400.0490.0620.0430.0480.0440.0500.0590.0510.0580.0610.0450.0000.0480.025
BEH_0200.1600.0470.4841.0000.4610.7300.0550.0330.0560.0590.0600.0590.0230.0480.0650.0650.0680.0610.0390.0590.2430.1120.1360.1340.0730.1230.0850.0550.0590.0640.1130.0730.1620.0830.0290.1420.0890.0440.0990.1180.0420.1170.0980.0770.0670.0560.0670.0730.0580.0520.0550.1290.0840.0650.0660.0520.1490.1300.1190.1010.1290.0580.0500.0650.0720.0660.0700.0700.0800.1190.1220.0420.0420.0370.0340.1080.1150.0570.0710.0910.0580.0660.0510.0700.0640.0560.0550.0430.0410.0570.0650.0610.0460.0520.0580.0290.0670.0620.0740.0380.0380.0740.0630.0640.0401.0000.0770.1770.0780.0630.0430.0450.1020.0520.0840.0600.0740.0750.0600.0580.1020.0910.0950.1130.1070.1260.1130.1150.1150.1100.0090.0810.060
BEH_0300.1670.0480.5360.4611.0000.5630.0260.0400.0560.0580.0790.0700.0240.0560.0660.0770.0590.0500.0400.0380.1940.0820.1110.1170.0770.1440.0810.0500.0550.0930.0970.0820.1220.0710.0290.1570.0620.0290.1070.1290.0260.1500.1190.0990.0860.0740.0680.0650.0390.0370.0590.1000.0670.0540.0600.0480.1620.1470.1330.1230.1430.0610.0580.0540.0580.0850.1000.0910.1090.0790.0750.0400.0410.0380.0340.0650.0690.0550.0660.0710.0370.0650.0550.0630.0620.0550.0470.0380.0440.0530.0650.0600.0460.0520.0560.0740.0590.0560.0630.0480.0500.0660.0480.0590.0531.0000.0710.1950.0640.0680.0520.0600.0880.0390.0890.0630.0940.0790.0630.0470.1090.0820.0850.1490.1410.1460.1490.1620.1640.1380.0000.0800.051
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BZP_0100.0340.0080.0600.0590.0380.0570.5940.0520.0210.0110.0360.0420.0350.1170.0730.0570.1240.1570.1221.0000.0750.0300.0720.2280.0250.0670.4540.0910.1030.1050.1190.0360.0620.0110.0170.1090.0150.0080.0880.0970.0020.0600.0730.0960.0250.0190.0180.0200.3430.0110.1020.2800.0800.6810.5540.1010.0460.0900.0910.0870.0630.0270.0430.0360.0530.0320.0220.0460.0320.0340.0510.0470.0560.0190.0420.0360.0450.4280.4480.2850.4500.3360.2140.3590.3440.2600.3230.2640.2350.4730.4630.4220.2820.3470.4120.0030.2920.3430.1850.0510.0470.2360.6200.1410.0391.0000.1070.0660.1590.0930.0380.0330.3620.8000.1360.1250.0570.1350.1020.7280.0830.1280.1400.0590.0760.0760.0560.0700.0660.0530.0000.4590.101
CAN_0100.4560.2420.0560.2430.1940.2480.0800.0040.0590.0680.0870.0470.0510.0500.1090.1240.0790.0520.1150.0751.0000.4570.7810.7520.2930.6970.1860.1810.1440.2450.3530.2080.4500.1540.0270.3110.0900.1040.2320.5730.0100.7070.5120.4540.3590.2560.1030.2390.1420.3460.1380.3660.3120.0850.1250.1540.3480.3870.3060.2770.4580.1720.0880.1830.1610.2480.2260.1990.2170.3550.3140.0690.0690.1460.0950.3290.3140.1180.1510.2510.0720.1530.1560.1460.1450.1310.1290.0970.1010.0980.1220.1130.1120.1130.1240.0730.1560.1010.1780.0960.1030.2390.1050.1540.0961.0000.1960.5650.1940.1540.1180.0900.3350.0690.4040.2970.4090.2780.2450.1030.4630.2710.2700.6370.6180.6670.6290.6230.6190.6530.0250.1930.201
CAN_0500.3720.5120.0310.1120.0820.1120.0270.0000.0750.0890.0830.0340.0400.0540.1130.1160.0890.0540.0640.0300.4571.0000.2510.1970.5370.2060.0870.4640.0730.1100.1640.1320.2050.0720.0340.1830.0810.1000.1050.1550.0110.2080.1430.1320.5600.5130.0440.1210.0620.2070.0590.1680.4850.0270.0560.4670.2470.2360.1810.1590.3100.1700.1710.1650.1660.2040.1960.2000.1920.2470.2370.1870.1810.1820.1830.2320.2310.0340.0610.0830.0230.0530.0450.0520.0440.0370.0440.0310.0280.0300.0440.0320.0310.0310.0390.0670.0740.0510.0770.4120.0710.0750.0420.0760.0581.0000.0910.2980.1020.0730.4190.0390.1350.0310.1150.0860.1090.0830.0650.0490.1800.1170.1190.2360.1700.1990.1730.1750.1740.1890.0240.0980.435
CAN_1300.3860.1270.0350.1360.1110.1430.0730.0290.0640.0670.0910.0650.0350.0710.1210.1230.0930.0770.0760.0720.7810.2511.0000.4080.1620.3520.1550.1120.1180.1730.2790.1170.2520.0760.0250.3390.0950.0640.1900.3150.0120.3630.2570.2400.1910.1370.0590.1300.1190.1730.1040.2950.1880.0780.1070.0930.3070.3820.2980.2750.4210.0870.0450.0980.0850.1260.1140.1040.1120.1880.1720.0380.0390.0730.0530.1810.1760.0850.1060.1710.0590.1090.1030.1050.0970.0910.0910.0710.0650.0730.0890.0870.0780.0830.0910.0410.1250.0980.1460.0530.0700.1600.0880.1290.0701.0000.1540.5200.1570.1230.0710.0600.2470.0640.2350.1700.2200.1660.1490.0940.2650.2060.2010.3390.3260.3340.3380.3360.3330.3300.0000.1600.119
CAN_1400.3830.1070.0490.1340.1170.1460.1690.0330.0500.0620.0800.0590.0350.0990.1000.1080.1010.1070.0740.2280.7520.1970.4081.0000.1320.5560.2380.1120.1310.2330.3590.1290.272-0.0610.0420.3880.0620.0600.223-0.5170.0140.3280.2320.2290.1570.1090.0750.1760.1690.2890.1180.3540.1660.2310.2310.0950.2830.4110.3610.3160.4590.0810.0590.0820.0760.1100.0990.0910.0960.1550.1440.0510.0530.0660.0570.1480.1460.2180.2160.2490.2060.1970.1620.2060.1850.1500.1750.1540.1310.2260.2310.2240.1510.1990.225-0.0220.2190.1910.1900.0520.0800.2620.2020.1660.081-0.0010.1690.5170.2020.1550.0620.0640.2780.2370.2490.2130.2050.2100.1880.2130.2870.2480.2430.3460.5310.5370.5430.5270.5270.353-0.0060.2330.112
CA_0200.2980.5550.0550.0730.0770.0740.0250.0280.0740.0840.0640.0430.0470.0470.0980.1070.0630.0480.0560.0250.2930.5370.1620.1321.0000.1610.0770.4090.0590.0990.1150.1290.1440.0590.0520.1710.0590.0760.0910.1350.0330.1560.1070.1010.6100.5740.0500.1010.0500.1440.0440.1180.4020.0240.0460.3960.2000.1870.1470.1270.2290.1960.2040.1640.1710.2020.1860.2090.1850.2060.2060.1890.1890.1990.2090.1840.1900.0250.0450.0600.0170.0430.0380.0360.0380.0310.0350.0250.0170.0230.0340.0250.0270.0260.0290.0490.0560.0410.0610.4030.0530.0590.0320.0580.0491.0000.0680.2570.0830.0560.4060.0350.0990.0220.0990.0710.0820.0650.0540.0380.1630.0940.0930.1880.1290.1470.1310.1320.1320.1400.0130.0780.370
CI_0100.5350.1370.0780.1230.1440.1320.0640.0360.0920.0980.1210.0770.0430.0760.1300.1540.1070.0730.0760.0670.6970.2060.3520.5560.1611.0000.1730.0960.1230.2000.2500.1460.248-0.0830.0690.4640.0990.0770.270-0.6720.0480.4840.3300.2930.2140.1600.1000.2310.1190.3360.1060.2630.1450.0730.1160.0860.4150.4100.3240.2930.4760.1020.0650.0900.0810.1510.1410.1250.1370.1520.1350.0410.0430.0810.0570.1400.1330.0740.0940.1390.0520.1080.0920.0930.0900.0800.0750.0610.0590.0620.0780.0830.0710.0700.076-0.0590.1310.0850.1470.0580.0940.2200.0910.1300.092-0.0070.1570.9360.1640.1340.0670.0910.2160.0630.2600.1940.2510.1730.1570.0790.3610.2030.2010.4460.7590.9230.7080.7180.7100.352-0.0210.1630.108
COC_0100.0980.0370.0620.0850.0810.0960.3460.0470.0380.0350.0630.0690.0380.1030.0920.0780.1140.1120.0890.4540.1860.0870.1550.2380.0770.1731.0000.1380.1270.1370.1810.0820.1330.0270.0130.2350.0400.0050.2070.2330.0160.1640.1070.1200.0610.0460.0410.0590.2780.0450.0970.3540.1260.4570.4980.1070.1030.1650.1780.1520.1350.0480.0460.0510.0520.0560.0510.0560.0530.0740.0780.0500.0520.0280.0390.0760.0790.3460.3620.3050.2690.5860.2290.3020.2760.2020.2630.1990.1540.3550.3210.4550.2240.2830.3280.0100.3050.2610.2410.0550.0450.2530.4430.2010.0491.0000.1590.2040.2250.1870.0590.0450.2960.4120.1820.1680.1250.1950.1590.4060.1860.2670.2820.1540.1350.1650.1740.1540.1580.1230.0000.4780.115
COC_0300.1760.3740.0310.0550.0500.0580.0680.0360.0700.0870.0770.0470.0490.0610.0690.0940.0690.0710.0480.0910.1810.4640.1120.1120.4090.0960.1381.0000.0680.1030.1170.0970.1130.0390.0720.1230.0390.0500.0930.0900.0080.1000.0740.0720.4020.3890.0390.0760.0710.0740.0570.1130.6380.0840.0980.6820.1240.1360.1170.1110.1630.1450.1410.1450.1420.1690.1710.1640.1670.1720.1660.1680.1590.1580.1530.1670.1650.0700.0820.0720.0580.0930.0490.0640.0600.0450.0580.0470.0390.0710.0750.0770.0500.0560.0640.0330.0910.0810.0910.5230.0580.0790.0890.0670.0541.0000.0730.1430.0980.0650.4860.0310.0920.0850.0770.0620.0640.0630.0530.0900.1080.1110.1100.1260.0820.0960.0890.0940.0940.0910.0090.1130.657
DEX_0100.1300.0480.0380.0590.0550.0620.1030.0420.0790.0730.0790.0720.0430.0680.0990.1010.0880.0760.0750.1030.1440.0730.1180.1310.0590.1230.1270.0681.0000.0900.1030.0760.1000.0140.0060.1410.0410.0080.0910.1310.0000.1160.0920.1080.0650.0560.0400.0800.1350.0350.3650.1520.0950.0910.1150.0700.1290.1580.1420.1340.1360.0330.0310.0390.0470.0450.0530.0460.0570.0580.0620.0470.0480.0260.0280.0610.0690.1060.1450.1590.0880.1220.1320.1600.1810.2330.1980.2910.1900.1080.1380.1140.1370.1550.1570.0160.1740.1260.2190.0670.0910.2230.1150.1560.0721.0000.4280.1510.2930.1280.0730.0530.1340.0880.1040.0900.0970.1010.0860.1060.1320.1760.1580.1260.1120.1270.1120.1150.1200.1110.0230.1530.083
DR_0100.2030.0870.0440.0640.0930.0750.0870.0250.0350.0230.0490.0460.0150.0730.1010.0720.0800.0850.0710.1050.2450.1100.1730.2330.0990.2000.1370.1030.0901.0000.4050.2050.2010.0510.0330.2330.0060.0320.1460.2160.0340.2150.1430.1610.1130.0870.0180.0440.1180.0770.0820.1570.1150.0990.1210.0860.1620.2530.2310.2000.2440.0610.0460.0530.0540.0780.0690.0820.0730.0770.0760.0350.0420.0440.0580.0760.0800.1520.1350.1600.1240.1410.1260.1440.1480.1120.1290.1000.0870.1280.1320.1470.1190.1330.1290.0180.1210.1100.1160.0500.0550.1520.1250.1030.0601.0000.1160.2510.1230.0960.0600.0420.1330.1080.2080.1610.1520.1220.1280.1120.2010.1550.1400.2140.1910.1960.2080.2060.2140.1860.0110.1300.096
DR_0200.1850.0780.0390.1130.0970.1310.0860.0300.0400.0400.0580.0630.0200.0640.0960.0850.0850.0770.0670.1190.3530.1640.2790.3590.1150.2500.1810.1170.1030.4051.0000.1470.3210.0500.0310.3030.0240.0340.1770.2740.0290.2670.1790.1840.1240.0850.0380.0780.1230.1140.0790.2730.1630.1180.1510.0880.1660.2500.2230.2010.2380.0670.0300.0650.0490.0830.0750.0700.0710.1270.1130.0300.0280.0490.0290.1260.1220.1610.1730.2270.1150.1770.1460.1730.1590.1160.1430.1110.0900.1510.1600.1700.1410.1420.1530.0360.1580.1210.1480.0390.0530.1890.1440.1350.0541.0000.1300.3250.1460.1230.0630.0430.2000.1150.2380.2000.1690.1870.1550.1290.2390.1870.1990.2500.2160.2260.2590.2420.2450.2220.0000.1850.115
DR_0600.2880.1560.0470.0730.0820.0720.0420.0380.1150.1240.1070.0810.0660.0690.1260.1540.0820.0520.0850.0360.2080.1320.1170.1290.1290.1460.0820.0970.0760.2050.1471.0000.3750.0950.0940.1440.0780.0820.0870.1210.0280.1480.1150.1210.1390.1270.0410.0970.0680.0680.0740.1070.1100.0510.0690.0940.1720.1950.1770.1520.2460.0880.0710.0800.0720.1000.1070.0900.1000.0970.0830.0680.0650.0950.0910.0890.0840.0580.0570.0720.0420.0690.0540.0640.0630.0600.0610.0520.0470.0520.0560.0570.0530.0570.0590.0450.0710.0610.0720.0930.0710.0850.0560.0670.0731.0000.1020.1920.0970.0690.1020.0360.1000.0420.0990.0730.1010.0700.0720.0460.1400.1190.1040.1670.1370.1430.1370.1360.1360.1420.0000.0760.094
DR_0700.2860.1190.0630.1620.1220.1650.0510.0460.1130.1200.1240.0900.0610.0720.1310.1610.1020.0700.0930.0620.4500.2050.2520.2720.1440.2480.1330.1130.1000.2010.3210.3751.0000.0830.0530.2530.0860.0730.1520.2210.0420.2650.1950.1880.1590.1190.0640.1300.0930.1180.0900.2290.1560.0600.0970.0910.2470.2760.2330.2150.3240.0820.0550.0870.0820.1100.1060.0890.1020.1470.1370.0480.0440.0680.0460.1420.1390.0810.0990.1390.0500.1000.0840.0970.0860.0770.0830.0690.0560.0670.0840.0810.0690.0720.0810.0520.1090.0730.1190.0610.0700.1220.0710.1080.0821.0000.1310.3550.1370.0990.0750.0520.1890.0580.1610.1340.1600.1300.1080.0790.2050.1700.1720.2500.2230.2390.2460.2370.2370.2350.0000.1320.108
DVDESCRIBE0.2180.0870.0750.0830.0710.0800.0000.0140.0660.0750.0390.0390.0460.0300.0130.0350.0160.0220.0230.0110.1540.0720.076-0.0610.059-0.0830.0270.0390.0140.0510.0500.0950.0831.0000.0290.0970.0690.3490.0610.0330.2150.0830.0560.0470.0740.0620.0630.0090.0230.0400.0400.0630.0520.0150.0150.0530.0960.0930.0640.0650.1230.0800.0600.0600.0460.0820.0780.0650.0630.0920.0770.0490.0420.1140.0900.0880.0750.0120.0130.0350.0030.0210.0200.0230.0100.0080.0070.0160.0160.0080.0100.0090.0110.0070.0080.1990.0270.0060.0280.0470.0180.0120.0230.0280.0300.0110.0350.1400.0150.0230.0530.0760.0370.0090.0430.0300.0390.0380.0290.0140.0760.0390.0390.097-0.078-0.091-0.084-0.078-0.082-0.0540.1800.0230.047
DVGENDER0.0500.0750.0190.0290.0290.0320.0030.0320.0870.2140.1060.0000.1130.0450.0360.0810.0000.0250.0460.0170.0270.0340.0250.0420.0520.0690.0130.0720.0060.0330.0310.0940.0530.0291.0000.0120.2100.0570.0190.0200.0040.0790.0280.0710.0340.0450.0490.2480.0090.0000.0330.0110.0490.0130.0120.0600.0280.0250.0250.0390.0970.0700.0670.0680.0730.0560.0550.0740.0280.0820.1020.0710.0780.0770.0960.0880.1050.0000.0120.0000.0080.0080.0000.0180.0000.0240.0180.0060.0280.0120.0120.0070.0050.0060.0100.0460.0000.0000.0100.0670.0620.0330.0050.0030.0571.0000.0290.0000.0240.0170.0590.0540.0100.0240.0610.0500.0660.0000.0090.0180.0510.0260.0160.1070.1070.0740.0830.0870.0860.0820.0330.0050.056
DVLAST300.1680.0840.0970.1420.1570.1660.0920.0330.0370.0290.0580.0600.0340.0630.0780.0730.0710.0540.0730.1090.3110.1830.3390.3880.1710.4640.2350.1230.1410.2330.3030.1440.2530.0970.0121.0000.0660.0340.6471.0000.0440.4380.2530.2760.1490.0930.0830.1160.1440.1130.1060.3330.1830.0990.1800.0950.1800.2450.2320.2040.2150.1070.0990.0810.0840.1330.1140.1180.1140.1530.1420.0390.0340.0640.0530.1450.1490.1640.2180.2880.0780.2180.1850.2190.1830.1280.1670.1320.1010.1400.1840.1630.1690.1800.1820.0480.2020.1290.1940.0310.0610.2460.1340.1700.0781.0000.1450.4370.2040.1650.0540.0610.2320.0840.4370.3440.2450.3120.3040.1330.7020.2310.2600.4110.3310.3700.4310.3800.3740.3310.0000.2400.125
DVORIENT0.0830.0600.0540.0890.0620.0690.0260.0230.0950.1240.0800.0390.1170.0000.0210.0650.0160.0140.0430.0150.0900.0810.0950.0620.0590.0990.0400.0390.0410.0060.0240.0780.0860.0690.2100.0661.0000.0550.0500.0870.0060.0750.0590.0790.0550.0510.1810.3020.0610.0380.0320.0650.0560.0090.0310.0410.1030.0770.0570.0440.0590.0730.0380.0710.0620.0600.0680.0460.0660.0980.1000.0530.0310.0490.0380.1050.1000.0250.0260.0460.0200.0280.0360.0560.0320.0370.0540.0250.0180.0270.0240.0280.0300.0450.0430.0430.0430.0230.0450.0500.0260.0880.0270.0650.0621.0000.0730.0750.0900.0450.0700.0660.0620.0040.0110.0040.0440.0340.0460.0220.1290.0850.0710.1150.0790.0930.0850.0890.0880.0810.0300.0370.047
DVRES0.0970.0890.0190.0440.0290.0370.0000.0260.0460.0520.0300.0110.0470.0140.0140.0240.0080.0050.0200.0080.1040.1000.0640.0600.0760.0770.0050.0500.0080.0320.0340.0820.0730.3490.0570.0340.0551.0000.0150.0320.1270.0810.0490.0440.0960.0800.0240.0410.0050.0160.0380.0270.0640.0000.0000.0620.0480.0310.0090.0000.0690.0730.0600.0730.0530.0850.0730.0680.0580.1010.0810.0570.0580.0930.0790.0950.0770.0020.0070.0190.0000.0120.0160.0000.0000.0000.0000.0050.0150.0000.0070.0100.0060.0040.0050.0850.0060.0000.0090.0520.0130.0100.0000.0080.0141.0000.0130.0430.0040.0180.0670.0530.0270.0000.0320.0180.0410.0080.0000.0000.0480.0210.0180.0790.0630.0750.0640.0640.0660.0640.0140.0080.054
DVTY1ST0.1250.0380.0820.0990.1070.1190.0700.0200.0190.0130.0370.0470.0250.0420.0470.0480.0470.0480.0460.0880.2320.1050.1900.2230.0910.2700.2070.0930.0910.1460.1770.0870.1520.0610.0190.6470.0500.0151.0001.0000.0470.2330.1260.1510.0720.0470.0510.0680.0950.0690.0670.2140.1170.0730.1510.0750.1350.1740.1880.1700.1480.0540.0590.0420.0480.0620.0500.0660.0590.0780.0840.0170.0260.0240.0260.0750.0910.1440.1980.2010.0610.1970.1280.1800.1360.0790.1170.0860.0590.1350.1670.1720.1370.1220.1570.0260.1440.1090.1370.0230.0450.1600.1200.1510.0541.0000.1010.3170.1380.1100.0400.0480.1420.0740.2810.2420.1430.2220.2240.1030.4180.1670.1750.2130.1800.2900.2800.2290.2270.1670.0000.2110.091
DVTY2ST0.3530.0820.0860.1180.1290.1340.0820.0390.0690.0600.0930.0760.0300.0810.1150.1170.1010.0740.0630.0970.5730.1550.315-0.5170.135-0.6720.2330.0900.1310.2160.2740.1210.2210.0330.0201.0000.0870.0321.0001.0000.0610.3430.2300.2120.1350.093-0.110-0.1710.130-0.2320.1050.3060.1330.0960.1650.0720.3190.3650.3180.2870.3530.0890.0650.0690.0670.1070.0920.0930.0940.1190.1120.0270.0290.0490.0360.1120.1130.1160.1540.1780.0580.1540.1150.1440.1150.0870.1050.0820.0620.1080.1260.1320.1100.1050.1230.0260.1750.1280.1790.0340.074-0.2470.1400.1740.082-0.0030.1551.0000.1880.1500.0450.0700.2250.0880.3240.2550.2140.2290.2170.1210.4850.2310.2360.352-0.540-0.558-0.557-0.533-0.534-0.3560.0210.2350.094
DVURBAN0.0100.0040.0540.0420.0260.0350.0080.0180.0480.0460.0180.0240.0550.0000.0000.0070.0120.0000.0160.0020.0100.0110.0120.0140.0330.0480.0160.0080.0000.0340.0290.0280.0420.2150.0040.0440.0060.1270.0470.0611.0000.0340.0140.0000.0050.0310.0320.0000.0080.0800.0250.0120.0220.0000.0070.0310.0280.0260.0000.0000.0000.0530.0610.0100.0220.0400.0210.0440.0250.0460.0430.0140.0040.0320.0310.0450.0430.0050.0160.0170.0140.0120.0170.0080.0010.0000.0140.0000.0170.0000.0000.0110.0000.0060.0000.2600.0070.0000.0000.0240.0000.0000.0140.0120.0121.0000.0060.0390.0040.0000.0320.0230.0200.0030.0180.0260.0030.0180.0070.0000.0330.0000.0180.0320.0350.0490.0500.0540.0550.0320.0450.0000.034
ELC_026a0.5130.1340.0430.1170.1500.1300.0580.0300.0970.1020.1340.0720.0420.0770.1390.1650.1120.0630.0800.0600.7070.2080.3630.3280.1560.4840.1640.1000.1160.2150.2670.1480.2650.0830.0790.4380.0750.0810.2330.3430.0341.0000.3640.3890.2140.1550.0610.1290.1060.1620.1170.2620.1480.0670.1140.0850.4140.4380.3430.3100.5020.1000.0560.0920.0810.1590.1440.1340.1450.1570.1360.0390.0380.0800.0560.1440.1370.0750.0930.1420.0390.0990.0960.0910.0850.0750.0750.0630.0610.0640.0770.0710.0680.0700.0780.0460.1230.0940.1410.0550.0940.1310.0780.1220.0871.0000.1580.6280.1580.1320.0650.0770.2220.0490.2380.1770.3850.1660.1530.0770.3420.1980.1980.5750.4350.4900.5190.4630.4630.4950.0020.1630.104
ELC_026b0.3700.0970.0430.0980.1190.1060.0670.0480.0740.0770.1020.0640.0290.0780.1050.1200.0870.0640.0640.0730.5120.1430.2570.2320.1070.3300.1070.0740.0920.1430.1790.1150.1950.0560.0280.2530.0590.0490.1260.2300.0140.3641.0000.3380.1450.1210.0400.0870.0800.1260.0950.1840.1090.0760.0820.0680.3060.3120.2510.2340.3690.0650.0450.0650.0630.1020.1010.0920.0980.1070.0970.0310.0310.0510.0390.0990.0960.0670.0800.1010.0560.0720.0730.0720.0720.0650.0700.0560.0640.0640.0790.0710.0620.0660.0660.0340.0890.0860.1140.0450.0740.1040.0920.0850.0731.0000.1220.4440.1350.1030.0550.0610.1680.0640.1890.1500.2060.1570.1420.0820.2110.1550.1540.3230.2960.3430.2890.2870.2820.3150.0000.1170.084
ELC_026c0.3190.0890.0310.0770.0990.0870.0750.0470.0860.1020.1110.0800.0410.0900.1330.1570.1210.0690.0770.0960.4540.1320.2400.2290.1010.2930.1200.0720.1080.1610.1840.1210.1880.0470.0710.2760.0790.0440.1510.2120.0000.3890.3381.0000.1360.1030.0490.0960.1200.0990.0980.1850.1010.0930.1330.0720.2640.3200.2770.2610.3560.0680.0410.0680.0610.1020.0930.0880.0930.0980.0870.0340.0330.0500.0420.0890.0860.0970.1000.1160.0650.0850.0870.1000.0960.0850.1010.0750.0630.0910.1020.0840.0710.0870.0960.0420.1090.1070.1310.0430.0690.1140.0900.0970.0821.0000.1400.3850.1520.1080.0550.0570.1840.0720.1860.1440.2100.1460.1690.0830.2410.2040.1940.3330.2620.3050.2780.2690.2650.2880.0000.1280.081
ELC_0410.4010.5260.0330.0670.0860.0730.0220.0160.0710.0850.0810.0330.0430.0500.1120.1260.0720.0330.0680.0250.3590.5600.1910.1570.6100.2140.0610.4020.0650.1130.1240.1390.1590.0740.0340.1490.0550.0960.0720.1350.0050.2140.1450.1361.0000.7530.0490.1040.0530.1940.0550.1190.4160.0230.0450.4000.2690.2200.1730.1470.3060.1710.1810.1660.1730.2260.2180.2260.2140.2130.2090.1900.1880.1840.1910.2020.2020.0280.0420.0600.0180.0410.0350.0370.0380.0300.0360.0250.0200.0250.0330.0260.0230.0260.0280.0610.0550.0450.0600.3900.0670.0580.0320.0580.0561.0000.0790.2650.0830.0610.4000.0400.1030.0250.1040.0740.1090.0670.0550.0360.1700.1030.0980.2480.1800.2100.1750.1780.1770.2000.0140.0720.381
ELC_0420.3370.5030.0300.0560.0740.0580.0250.0190.0570.0730.0610.0370.0370.0350.0870.1010.0540.0270.0560.0190.2560.5130.1370.1090.5740.1600.0460.3890.0560.0870.0850.1270.1190.0620.0450.0930.0510.0800.0470.0930.0310.1550.1210.1030.7531.0000.0540.0910.0410.1670.0480.0820.3990.0180.0350.3910.2140.1660.1340.1190.2410.1660.1820.1650.1770.2120.2170.2180.2160.1980.1990.1900.1900.1780.1900.1900.1950.0230.0340.0430.0160.0300.0270.0290.0310.0250.0320.0240.0190.0210.0290.0210.0200.0230.0240.0460.0430.0390.0480.3910.0600.0480.0310.0480.0481.0000.0590.1830.0670.0460.4020.0360.0780.0190.0810.0550.0780.0510.0400.0230.1320.0760.0680.1900.1290.1570.1240.1290.1290.1460.0120.0550.375
GH_0100.0640.0480.0710.0670.0680.0610.0180.0420.0770.0810.0670.0640.0470.0400.0430.0510.0340.0320.0270.0180.1030.0440.0590.0750.0500.1000.0410.0390.0400.0180.0380.0410.0640.0630.0490.0830.1810.0240.051-0.1100.0320.0610.0400.0490.0490.0541.0000.4200.0410.0580.0230.0560.0440.0210.0390.0360.1090.0710.0500.0620.0480.0600.0510.0570.0540.0610.0610.0580.0630.0690.0670.0450.0380.0430.0390.0650.0660.0270.0250.0320.0100.0270.0310.0260.0150.0300.0310.0220.0210.0180.0210.0260.0170.0230.024-0.0000.0400.0260.0430.0420.0360.0620.0240.0370.0480.0050.0520.1140.0570.0390.0420.0600.0490.0120.0320.0280.0370.0310.0240.0160.0800.0500.0520.0750.1070.0980.1120.1100.1100.0760.0060.0400.038
GH_0200.2180.1020.0380.0730.0650.0730.0190.0730.2140.2760.2120.1230.1190.0380.0790.1300.0730.0420.0620.0200.2390.1210.1300.1760.1010.2310.0590.0760.0800.0440.0780.0970.1300.0090.2480.1160.3020.0410.068-0.1710.0000.1290.0870.0960.1040.0910.4201.0000.0720.1780.0640.1010.0960.0240.0470.0730.2130.1730.1260.1230.1870.0730.0530.0760.0620.0820.0810.0660.0760.1040.0920.0510.0440.0670.0520.0980.0900.0290.0380.0600.0120.0370.0350.0470.0350.0470.0540.0320.0310.0200.0280.0230.0230.0410.0400.0070.0690.0370.0750.0730.0770.1020.0350.0780.1050.0000.1090.1850.1120.0540.0840.0630.0950.0160.0440.0370.0780.0510.0450.0280.1410.1100.1000.1670.2140.2380.2130.2120.2080.110-0.0210.0630.077
GLU_0100.0970.0320.0390.0580.0390.0550.3320.0780.0710.0590.0770.1020.0680.1140.1200.1040.1100.1580.0990.3430.1420.0620.1190.1690.0500.1190.2780.0710.1350.1180.1230.0680.0930.0230.0090.1440.0610.0050.0950.1300.0080.1060.0800.1200.0530.0410.0410.0721.0000.0230.1150.2090.0860.3280.2980.0690.1100.1510.1440.1180.1160.0350.0320.0380.0440.0460.0450.0480.0470.0540.0600.0370.0400.0250.0370.0490.0610.2390.2260.1910.2290.2090.1530.2220.2170.1990.2530.1840.1650.2310.2390.2300.1810.2360.2600.0170.2010.1870.1670.0520.0600.2120.3750.1360.0651.0000.1710.1350.1980.1130.0550.0340.2270.3540.1110.1130.0880.1040.1230.3150.1340.2300.2030.1130.1150.1220.1110.1110.1080.1010.0000.2530.084
GRADE0.3820.1580.0170.0520.0370.0510.0170.0950.0810.0450.0380.0870.0450.0340.0100.0220.0160.0240.0100.0110.3460.2070.1730.2890.1440.3360.0450.0740.0350.0770.1140.0680.1180.0400.0000.1130.0380.0160.069-0.2320.0800.1620.1260.0990.1940.1670.0580.1780.0231.0000.0220.0950.1170.0110.0250.0800.1660.1650.1320.1170.2710.0460.0580.0620.0600.0820.0720.0760.0680.1220.1100.0690.0690.0560.0550.1220.1150.0100.0320.0410.0000.0280.0210.0140.0200.0050.0110.0020.0060.0120.0170.0170.0160.0060.0130.0480.0310.0150.0380.0580.0650.0650.0100.0310.043-0.0020.0410.2350.0410.0350.0600.0520.0650.0040.1000.0650.0710.0520.0420.0200.1170.0480.0540.1610.2540.3220.2350.2340.2310.0860.1120.0510.095
GRV_0100.1230.0400.0300.0550.0590.0640.0990.0520.0780.0770.0840.0810.0480.0720.0710.0820.0830.0790.0520.1020.1380.0590.1040.1180.0440.1060.0970.0570.3650.0820.0790.0740.0900.0400.0330.1060.0320.0380.0670.1050.0250.1170.0950.0980.0550.0480.0230.0640.1150.0221.0000.0880.0650.0900.0840.0600.1270.1540.1280.1300.1310.0320.0290.0350.0380.0460.0480.0460.0550.0470.0490.0490.0550.0210.0300.0450.0540.0950.1030.0920.0960.1020.0990.1280.1250.1920.1440.1700.3910.1000.1100.0990.0930.1190.1180.0170.1050.1210.1360.0480.0840.1510.0970.1030.0751.0000.3370.1250.2250.0960.0610.0550.1240.1040.0860.0750.1040.0710.0770.0840.1120.1250.1170.1200.1190.1190.1170.1200.1200.1150.0050.0930.063
HAL_0100.1890.0730.0500.1290.1000.1480.2250.0300.0440.0470.0590.0660.0310.0860.1100.0910.1040.0900.0830.2800.3660.1680.2950.3540.1180.2630.3540.1130.1520.1570.2730.1070.2290.0630.0110.3330.0650.0270.2140.3060.0120.2620.1840.1850.1190.0820.0560.1010.2090.0950.0881.0000.2330.2640.3100.0950.1690.2580.2310.2130.2510.0720.0460.0770.0680.0930.0820.0730.0790.1360.1250.0410.0350.0460.0310.1380.1390.2110.2650.4920.1600.2390.2070.2460.2190.1680.2020.1630.1170.2010.2260.2110.1770.1820.2290.0390.2310.1960.2270.0500.0620.2670.3010.2070.0641.0000.1680.3400.2240.1890.0710.0620.3250.2760.2100.1900.1800.2250.1610.2980.2510.2970.3040.2440.2130.2280.2550.2410.2410.2110.0000.3980.138
HAL_0300.2530.3590.0310.0840.0670.0910.0630.0290.0740.0770.0800.0430.0420.0740.1030.1020.0990.0880.0580.0800.3120.4850.1880.1660.4020.1450.1260.6380.0950.1150.1630.1100.1560.0520.0490.1830.0560.0640.1170.1330.0220.1480.1090.1010.4160.3990.0440.0960.0860.1170.0650.2331.0000.0730.0960.6630.1890.2030.1620.1480.2320.1330.1300.1490.1490.1660.1720.1630.1710.1790.1780.1680.1610.1430.1430.1840.1870.0630.0910.1270.0530.0780.0660.0820.0710.0640.0740.0650.0400.0620.0750.0630.0580.0620.0720.0350.1050.0830.1080.5040.0680.1000.0890.1000.0661.0000.1070.2310.1280.0910.4890.0430.1460.0790.1040.0800.0870.0840.0660.0950.1510.1510.1440.1730.1230.1390.1310.1330.1320.1320.0120.1340.714
HER_0100.0430.0140.0660.0650.0540.0540.5360.0770.0380.0250.0580.0690.0470.1370.0910.0720.1330.1720.1170.6810.0850.0270.0780.2310.0240.0730.4570.0840.0910.0990.1180.0510.0600.0150.0130.0990.0090.0000.0730.0960.0000.0670.0760.0930.0230.0180.0210.0240.3280.0110.0900.2640.0731.0000.5700.0900.0550.1060.1060.0960.0700.0370.0530.0420.0580.0410.0290.0590.0400.0360.0560.0640.0700.0210.0470.0370.0520.4300.3660.2640.5080.3000.2090.3040.3280.2190.3080.2400.2120.4480.4080.4190.2570.3390.3650.0100.2460.2920.1740.0470.0540.2080.5750.1210.0571.0000.1150.0780.1700.0970.0400.0320.2930.6810.1630.1660.0760.1640.1320.5720.0830.1550.1520.0630.1290.0820.0720.0830.0810.0570.0000.4160.081
MET_0100.0650.0180.0560.0660.0600.0680.4350.0550.0320.0250.0440.0550.0380.1280.0780.0730.1170.1250.0970.5540.1250.0560.1070.2310.0460.1160.4980.0980.1150.1210.1510.0690.0970.0150.0120.1800.0310.0000.1510.1650.0070.1140.0820.1330.0450.0350.0390.0470.2980.0250.0840.3100.0960.5701.0000.1170.0720.1310.1220.1200.0880.0370.0420.0400.0470.0400.0330.0490.0440.0480.0610.0530.0560.0110.0350.0500.0600.5410.3560.2790.3150.3570.2710.2930.2670.1780.2680.1960.1600.4750.3660.3690.2480.2940.3060.0060.2880.2840.2050.0510.0430.2280.4970.1870.0541.0000.1410.1320.2200.2100.0590.0490.2930.5530.1680.1450.0930.1770.1490.5080.1460.2210.2250.1130.1180.1170.1160.1190.1150.0890.0000.4760.103
MET_0300.1820.3600.0320.0520.0480.0520.0770.0430.0680.0790.0730.0450.0440.0600.0670.0840.0670.0830.0480.1010.1540.4670.0930.0950.3960.0860.1070.6820.0700.0860.0880.0940.0910.0530.0600.0950.0410.0620.0750.0720.0310.0850.0680.0720.4000.3910.0360.0730.0690.0800.0600.0950.6630.0900.1171.0000.1280.1310.1040.1040.1550.1400.1370.1480.1460.1670.1750.1620.1680.1720.1660.1710.1640.1570.1480.1730.1690.0810.0840.0710.0690.0750.0490.0700.0600.0530.0710.0550.0510.0790.0810.0760.0540.0640.0720.0390.0950.0890.0830.5180.0600.0800.1020.0720.0521.0000.0770.1140.0990.0630.4830.0320.0910.0910.0650.0530.0540.0560.0510.0890.0950.1020.0930.1140.0750.0880.0750.0780.0780.0820.0110.1070.775
NRG_0100.3420.1700.0790.1490.1620.1530.0540.0560.1100.1080.1300.0830.0910.0820.1200.1510.1090.0570.1450.0460.3480.2470.3070.2830.2000.4150.1030.1240.1290.1620.1660.1720.2470.0960.0280.1800.1030.0480.1350.3190.0280.4140.3060.2640.2690.2140.1090.2130.1100.1660.1270.1690.1890.0550.0720.1281.0000.3810.2440.1920.2670.1380.0960.1420.1410.2100.2090.2020.2170.2100.2070.0630.0590.1160.0880.1960.2030.0660.0700.1260.0450.0900.0940.0790.0890.0980.0910.0770.0830.0570.0600.0670.0740.0720.0720.0700.0850.0670.1090.0960.1160.1400.0600.0790.1021.0000.1640.3130.1420.1000.1160.0880.1480.0380.2170.1660.2470.1440.1250.0600.3240.1430.1400.4240.3770.4160.3720.3710.3690.4050.0040.1000.150
NRG_0200.3600.1660.0660.1300.1470.1460.0960.0390.0750.0730.1030.0580.0630.0800.1200.1190.1090.0660.1180.0900.3870.2360.3820.4110.1870.4100.1650.1360.1580.2530.2500.1950.2760.0930.0250.2450.0770.0310.1740.3650.0260.4380.3120.3200.2200.1660.0710.1730.1510.1650.1540.2580.2030.1060.1310.1310.3811.0000.4980.3570.3940.1250.0820.1260.1240.1660.1610.1510.1670.1800.1710.0610.0670.0990.0950.1740.1760.1140.1240.2150.0840.1430.1680.1440.1450.1570.1590.1310.1230.0920.1160.1090.1220.1340.1300.0290.1390.1030.1640.0780.1200.2150.1140.1240.1191.0000.1980.3470.2160.1540.1070.0920.2250.0770.2900.2250.2950.2070.1940.0990.3510.2270.2170.4400.4020.4060.4150.4100.4100.4090.0000.1610.153
NRG_0300.2620.1280.0650.1190.1330.1300.1020.0320.0490.0490.0810.0500.0520.0560.0910.0900.0840.0600.0890.0910.3060.1810.2980.3610.1470.3240.1780.1170.1420.2310.2230.1770.2330.0640.0250.2320.0570.0090.1880.3180.0000.3430.2510.2770.1730.1340.0500.1260.1440.1320.1280.2310.1620.1060.1220.1040.2440.4981.0000.3910.3500.0920.0570.0930.0910.1220.1210.1100.1320.1310.1290.0450.0480.0610.0720.1270.1310.1490.1560.2140.1120.1710.1640.1750.1730.1570.1870.1490.1270.1430.1420.1550.1390.1640.1590.0250.1580.1260.1660.0610.1020.2170.1140.1330.1111.0000.1700.2850.2120.1420.0870.0860.2070.0840.2740.2300.2420.2090.1970.0940.3020.2220.2090.3370.3190.3250.3260.3260.3280.3170.0000.1490.125
NRG_0400.2520.1150.0510.1010.1230.1130.0790.0330.0650.0600.0890.0550.0480.0620.0990.0910.0910.0590.0860.0870.2770.1590.2750.3160.1270.2930.1520.1110.1340.2000.2010.1520.2150.0650.0390.2040.0440.0000.1700.2870.0000.3100.2340.2610.1470.1190.0620.1230.1180.1170.1300.2130.1480.0960.1200.1040.1920.3570.3911.0000.3680.0810.0500.0840.0810.1080.1070.1000.1180.1150.1090.0460.0530.0610.0670.1110.1130.1250.1250.1940.0980.1520.1420.1490.1380.1290.1410.1070.1250.1100.1250.1250.1190.1360.1390.0240.1360.0960.1520.0620.0930.1810.1100.1120.1071.0000.1620.2630.1690.1280.0790.0720.2080.0760.2440.1960.2390.2020.2030.0960.2700.2000.1980.3100.2870.2960.2970.2940.2940.2840.0000.1390.119
NRG_0500.4620.2540.0310.1290.1430.1440.0600.0110.0680.0740.0940.0390.0600.0590.1080.1220.0980.0530.1130.0630.4580.3100.4210.4590.2290.4760.1350.1630.1360.2440.2380.2460.3240.1230.0970.2150.0590.0690.1480.3530.0000.5020.3690.3560.3060.2410.0480.1870.1160.2710.1310.2510.2320.0700.0880.1550.2670.3940.3500.3681.0000.1240.0740.1350.1280.1900.1890.1580.1850.2040.1810.0690.0710.1340.1090.1910.1850.0880.1090.1920.0640.1030.1440.1320.1290.1230.1250.0970.0940.0850.1010.0880.1020.1080.1110.0470.1240.0980.1500.1110.1180.1870.0910.1260.1221.0000.1790.3420.1750.1350.1350.0710.2240.0540.2790.2050.3130.1900.1820.0870.3540.1930.1850.4910.4480.4680.4450.4450.4440.4700.0000.1390.180
PH_0100.1550.1980.0610.0580.0610.0560.0270.0220.0430.0700.0350.0340.0330.0480.0530.0680.0520.0530.0370.0270.1720.1700.0870.0810.1960.1020.0480.1450.0330.0610.0670.0880.0820.0800.0700.1070.0730.0730.0540.0890.0530.1000.0650.0680.1710.1660.0600.0730.0350.0460.0320.0720.1330.0370.0370.1400.1380.1250.0920.0810.1241.0000.6090.5500.4200.6170.5230.4960.4410.5210.4490.4170.3820.5480.5010.4560.3990.0310.0270.0390.0260.0320.0230.0230.0270.0300.0280.0230.0220.0350.0260.0300.0240.0230.0230.0600.0390.0420.0450.1470.0360.0420.0270.0320.0381.0000.0500.1730.0450.0350.1490.0400.0530.0340.0560.0470.0560.0380.0360.0320.1330.0570.0530.1370.0890.0920.0880.0860.0860.0940.0130.0440.126
PH_0200.1080.2110.0720.0500.0580.0480.0360.0370.0430.0620.0390.0460.0300.0570.0470.0560.0400.0590.0330.0430.0880.1710.0450.0590.2040.0650.0460.1410.0310.0460.0300.0710.0550.0600.0670.0990.0380.0600.0590.0650.0610.0560.0450.0410.1810.1820.0510.0530.0320.0580.0290.0460.1300.0530.0420.1370.0960.0820.0570.0500.0740.6091.0000.4610.6120.4910.3740.6390.4480.4390.5390.4750.5240.4440.5990.3860.4770.0420.0370.0350.0370.0380.0240.0290.0350.0350.0370.0300.0250.0450.0400.0460.0310.0320.0330.0350.0400.0510.0390.1500.0410.0390.0400.0290.0381.0000.0400.1100.0420.0320.1510.0540.0430.0450.0400.0380.0270.0350.0340.0400.1010.0570.0430.0760.0540.0560.0510.0530.0540.0530.0210.0380.122
PH_0300.1690.1590.0420.0650.0540.0620.0340.0350.0260.0540.0320.0400.0220.0500.0510.0590.0560.0530.0380.0360.1830.1650.0980.0820.1640.0900.0510.1450.0390.0530.0650.0800.0870.0600.0680.0810.0710.0730.0420.0690.0100.0920.0650.0680.1660.1650.0570.0760.0380.0620.0350.0770.1490.0420.0400.1480.1420.1260.0930.0840.1350.5500.4611.0000.6440.5290.4790.4550.4280.4970.4420.4240.4010.3870.4050.4920.4420.0320.0360.0400.0290.0350.0260.0330.0320.0260.0320.0240.0200.0340.0300.0290.0240.0290.0290.0470.0440.0440.0460.1510.0330.0400.0420.0410.0371.0000.0520.1350.0510.0410.1530.0350.0660.0410.0530.0430.0530.0500.0360.0420.1140.0600.0550.1270.0750.0880.0760.0750.0750.0890.0140.0520.146
PH_0400.1510.1660.0530.0720.0580.0640.0440.0380.0350.0540.0430.0500.0230.0630.0520.0660.0590.0640.0410.0530.1610.1660.0850.0760.1710.0810.0520.1420.0470.0540.0490.0720.0820.0460.0730.0840.0620.0530.0480.0670.0220.0810.0630.0610.1730.1770.0540.0620.0440.0600.0380.0680.1490.0580.0470.1460.1410.1240.0910.0810.1280.4200.6120.6441.0000.4310.3630.5750.4430.4080.5270.4580.5050.2980.4880.4100.5080.0430.0450.0400.0400.0420.0370.0400.0400.0380.0430.0330.0240.0440.0460.0450.0400.0410.0450.0400.0520.0620.0570.1510.0410.0500.0490.0510.0461.0000.0600.1220.0620.0500.1530.0460.0680.0580.0540.0470.0450.0510.0420.0540.1050.0690.0560.1080.0710.0810.0680.0690.0670.0800.0180.0570.145
PH_0510.2510.2100.0450.0660.0850.0700.0310.0280.0440.0680.0490.0440.0310.0590.0790.0930.0660.0470.0520.0320.2480.2040.1260.1100.2020.1510.0560.1690.0450.0780.0830.1000.1100.0820.0560.1330.0600.0850.0620.1070.0400.1590.1020.1020.2260.2120.0610.0820.0460.0820.0460.0930.1660.0410.0400.1670.2100.1660.1220.1080.1900.6170.4910.5290.4311.0000.6460.6430.5530.5580.4800.4400.4020.5060.4820.5070.4450.0340.0300.0480.0310.0350.0330.0340.0300.0340.0330.0260.0260.0340.0290.0320.0270.0270.0290.0480.0400.0410.0470.1720.0440.0450.0330.0430.0461.0000.0600.2080.0580.0480.1710.0430.0730.0350.0700.0570.0890.0500.0460.0470.1530.0740.0720.2040.1390.1510.1390.1360.1350.1510.0040.0520.156
PH_0520.2450.1920.0570.0700.1000.0750.0290.0190.0540.0760.0610.0390.0330.0500.0770.1070.0620.0470.0510.0220.2260.1960.1140.0990.1860.1410.0510.1710.0530.0690.0750.1070.1060.0780.0550.1140.0680.0730.0500.0920.0210.1440.1010.0930.2180.2170.0610.0810.0450.0720.0480.0820.1720.0290.0330.1750.2090.1610.1210.1070.1890.5230.3740.4790.3630.6461.0000.4860.6760.5040.4030.3850.3330.4950.3900.4710.3890.0260.0250.0440.0200.0310.0310.0290.0250.0360.0270.0280.0200.0230.0180.0220.0210.0240.0230.0300.0430.0340.0430.1730.0470.0420.0310.0360.0431.0000.0670.1800.0640.0450.1700.0370.0740.0290.0550.0440.0850.0420.0340.0380.1400.0680.0680.1940.1330.1420.1310.1290.1280.1420.0000.0450.165
PH_0610.2110.2160.0600.0700.0910.0730.0470.0410.0470.0650.0550.0530.0310.0670.0760.0800.0660.0650.0490.0460.1990.2000.1040.0910.2090.1250.0560.1640.0460.0820.0700.0900.0890.0650.0740.1180.0460.0680.0660.0930.0440.1340.0920.0880.2260.2180.0580.0660.0480.0760.0460.0730.1630.0590.0490.1620.2020.1510.1100.1000.1580.4960.6390.4550.5750.6430.4861.0000.5710.4670.5590.4830.5240.4120.5630.4260.5100.0440.0410.0510.0430.0410.0380.0370.0450.0450.0430.0350.0320.0450.0420.0430.0400.0360.0380.0280.0500.0540.0560.1710.0450.0490.0460.0420.0461.0000.0610.1750.0590.0560.1730.0600.0700.0520.0620.0520.0720.0510.0460.0440.1360.0740.0660.1720.1230.1280.1250.1230.1230.1290.0160.0500.153
PH_0620.2400.1860.0660.0800.1090.0840.0310.0280.0630.0770.0700.0580.0380.0650.0860.1060.0690.0660.0530.0320.2170.1920.1120.0960.1850.1370.0530.1670.0570.0730.0710.1000.1020.0630.0280.1140.0660.0580.0590.0940.0250.1450.0980.0930.2140.2160.0630.0760.0470.0680.0550.0790.1710.0400.0440.1680.2170.1670.1320.1180.1850.4410.4480.4280.4430.5530.6760.5711.0000.4480.4540.4040.3970.3970.4350.4240.4360.0350.0280.0440.0290.0320.0360.0310.0340.0400.0370.0330.0290.0300.0290.0260.0310.0290.0310.0260.0540.0530.0560.1730.0500.0510.0390.0440.0501.0000.0700.1820.0720.0570.1720.0520.0750.0330.0570.0480.0900.0500.0440.0400.1360.0760.0700.1890.1320.1410.1330.1300.1290.1410.0100.0490.162
PH_0700.2610.2090.0470.1190.0790.1110.0270.0210.0420.0570.0340.0280.0300.0490.0650.0800.0610.0550.0480.0340.3550.2470.1880.1550.2060.1520.0740.1720.0580.0770.1270.0970.1470.0920.0820.1530.0980.1010.0780.1190.0460.1570.1070.0980.2130.1980.0690.1040.0540.1220.0470.1360.1790.0360.0480.1720.2100.1800.1310.1150.2040.5210.4390.4970.4080.5580.5040.4670.4481.0000.6490.4340.3900.5210.4730.7140.5570.0320.0410.0690.0220.0470.0390.0390.0330.0350.0340.0300.0230.0320.0330.0340.0290.0310.0320.0970.0610.0430.0680.1730.0440.0610.0440.0560.0441.0000.0710.2270.0740.0580.1720.0430.1000.0380.0810.0640.0760.0550.0460.0490.1550.0870.0870.1900.1310.1470.1330.1320.1290.1440.0160.0730.161
PH_0800.2340.2090.0540.1220.0750.1150.0430.0430.0260.0520.0350.0440.0260.0690.0670.0710.0690.0770.0500.0510.3140.2370.1720.1440.2060.1350.0780.1660.0620.0760.1130.0830.1370.0770.1020.1420.1000.0810.0840.1120.0430.1360.0970.0870.2090.1990.0670.0920.0600.1100.0490.1250.1780.0560.0610.1660.2070.1710.1290.1090.1810.4490.5390.4420.5270.4800.4030.5590.4540.6491.0000.4650.4940.4160.5420.5570.7210.0420.0500.0670.0370.0500.0450.0440.0440.0390.0410.0370.0290.0440.0470.0460.0400.0350.0430.0880.0740.0580.0760.1730.0460.0660.0610.0590.0461.0000.0740.2060.0750.0650.1700.0530.0930.0540.0750.0640.0630.0540.0490.0550.1420.0860.0840.1630.1170.1320.1160.1180.1170.1290.0220.0780.158
PH_0900.0910.1900.0440.0420.0400.0390.0450.0350.0270.0550.0200.0440.0280.0540.0440.0510.0440.0690.0320.0470.0690.1870.0380.0510.1890.0410.0500.1680.0470.0350.0300.0680.0480.0490.0710.0390.0530.0570.0170.0270.0140.0390.0310.0340.1900.1900.0450.0510.0370.0690.0490.0410.1680.0640.0530.1710.0630.0610.0450.0460.0690.4170.4750.4240.4580.4400.3850.4830.4040.4340.4651.0000.6700.3610.4630.4350.4570.0340.0340.0320.0360.0350.0450.0290.0340.0380.0330.0350.0370.0440.0340.0350.0470.0320.0370.0360.0430.0520.0490.1880.0400.0370.0400.0380.0431.0000.0520.0430.0520.0600.1890.0560.0420.0500.0290.0280.0230.0320.0220.0390.0590.0520.0410.0640.0290.0420.0260.0300.0290.0350.0190.0430.161
PH_1000.0930.1940.0440.0420.0410.0420.0520.0550.0410.0540.0290.0430.0290.0660.0440.0470.0530.0810.0380.0560.0690.1810.0390.0530.1890.0430.0520.1590.0480.0420.0280.0650.0440.0420.0780.0340.0310.0580.0260.0290.0040.0380.0310.0330.1880.1900.0380.0440.0400.0690.0550.0350.1610.0700.0560.1640.0590.0670.0480.0530.0710.3820.5240.4010.5050.4020.3330.5240.3970.3900.4940.6701.0000.3190.5000.3860.4790.0400.0340.0330.0410.0370.0530.0340.0410.0500.0420.0400.0450.0480.0370.0410.0520.0360.0380.0370.0450.0570.0520.1790.0420.0470.0500.0300.0461.0000.0620.0420.0480.0670.1820.0610.0400.0590.0280.0330.0220.0300.0230.0490.0520.0400.0300.0540.0320.0460.0290.0290.0280.0340.0180.0410.156
PH_1100.2290.2260.0330.0370.0380.0360.0230.0170.0330.0630.0160.0090.0330.0170.0450.0660.0250.0120.0350.0190.1460.1820.0730.0660.1990.0810.0280.1580.0260.0440.0490.0950.0680.1140.0770.0640.0490.0930.0240.0490.0320.0800.0510.0500.1840.1780.0430.0670.0250.0560.0210.0460.1430.0210.0110.1570.1160.0990.0610.0610.1340.5480.4440.3870.2980.5060.4950.4120.3970.5210.4160.3610.3191.0000.5600.4400.3570.0100.0130.0220.0100.0230.0140.0110.0120.0190.0160.0150.0120.0160.0150.0210.0080.0150.0080.0460.0200.0180.0150.1570.0350.0190.0180.0180.0411.0000.0360.0960.0320.0210.1560.0400.0330.0260.0380.0260.0360.0160.0150.0270.0910.0350.0360.1170.0680.0770.0680.0670.0650.0720.0120.0160.139
PH_1200.1740.2290.0400.0340.0340.0340.0390.0330.0250.0560.0000.0330.0300.0520.0460.0500.0460.0620.0350.0420.0950.1830.0530.0570.2090.0570.0390.1530.0280.0580.0290.0910.0460.0900.0960.0530.0380.0790.0260.0360.0310.0560.0390.0420.1910.1900.0390.0520.0370.0550.0300.0310.1430.0470.0350.1480.0880.0950.0720.0670.1090.5010.5990.4050.4880.4820.3900.5630.4350.4730.5420.4630.5000.5601.0000.4070.4770.0380.0330.0290.0370.0340.0290.0280.0290.0320.0310.0270.0250.0370.0330.0350.0270.0290.0290.0310.0320.0480.0320.1590.0410.0290.0370.0220.0431.0000.0360.0630.0390.0350.1600.0500.0320.0430.0420.0410.0310.0280.0240.0330.0750.0360.0280.0840.0510.0550.0480.0470.0480.0520.0210.0240.133
PH_1300.2380.1850.0340.1080.0650.1040.0300.0220.0330.0610.0330.0330.0290.0450.0660.0720.0590.0540.0460.0360.3290.2320.1810.1480.1840.1400.0760.1670.0610.0760.1260.0890.1420.0880.0880.1450.1050.0950.0750.1120.0450.1440.0990.0890.2020.1900.0650.0980.0490.1220.0450.1380.1840.0370.0500.1730.1960.1740.1270.1110.1910.4560.3860.4920.4100.5070.4710.4260.4240.7140.5570.4350.3860.4400.4071.0000.6600.0340.0440.0710.0260.0490.0400.0410.0340.0350.0350.0290.0160.0300.0340.0320.0300.0270.0310.1130.0570.0400.0600.1750.0410.0560.0450.0550.0431.0000.0760.2110.0720.0630.1730.0390.0990.0370.0780.0640.0690.0560.0450.0490.1460.0860.0840.1740.1170.1340.1230.1200.1190.1320.0180.0720.169
PH_1400.2370.1900.0450.1150.0690.1100.0420.0370.0240.0490.0340.0430.0270.0620.0720.0740.0680.0730.0500.0450.3140.2310.1760.1460.1900.1330.0790.1650.0690.0800.1220.0840.1390.0750.1050.1490.1000.0770.0910.1130.0430.1370.0960.0860.2020.1950.0660.0900.0610.1150.0540.1390.1870.0520.0600.1690.2030.1760.1310.1130.1850.3990.4770.4420.5080.4450.3890.5100.4360.5570.7210.4570.4790.3570.4770.6601.0000.0450.0500.0720.0380.0510.0480.0470.0400.0420.0430.0340.0270.0420.0420.0410.0390.0340.0400.0920.0680.0530.0720.1730.0450.0640.0610.0590.0461.0000.0810.2050.0770.0720.1710.0480.1000.0480.0770.0670.0640.0620.0520.0580.1460.0880.0850.1630.1140.1310.1160.1180.1160.1290.0220.0810.168
POLY_0100.0600.0090.0620.0570.0550.0630.3040.0440.0230.0270.0380.0530.0260.1090.0770.0590.1150.1580.0790.4280.1180.0340.0850.2180.0250.0740.3460.0700.1060.1520.1610.0580.0810.0120.0000.1640.0250.0020.1440.1160.0050.0750.0670.0970.0280.0230.0270.0290.2390.0100.0950.2110.0630.4300.5410.0810.0660.1140.1490.1250.0880.0310.0420.0320.0430.0340.0260.0440.0350.0320.0420.0340.0400.0100.0380.0340.0451.0000.5660.4600.6130.5280.3740.4620.4300.3570.4840.3510.3140.6540.5030.5300.3840.4250.4670.0100.2840.3510.2260.0350.0510.2220.3510.1820.0601.0000.1420.1240.2090.1770.0340.0520.1930.4150.1270.1080.0670.1320.1240.3280.1190.2180.2310.0870.0930.1020.0830.0870.0990.0600.0000.3120.077
POLY_0200.0750.0230.0540.0710.0660.0750.3010.0420.0200.0190.0390.0480.0340.1070.0810.0660.1430.1570.0830.4480.1510.0610.1060.2160.0450.0940.3620.0820.1450.1350.1730.0570.0990.0130.0120.2180.0260.0070.1980.1540.0160.0930.0800.1000.0420.0340.0250.0380.2260.0320.1030.2650.0910.3660.3560.0840.0700.1240.1560.1250.1090.0270.0370.0360.0450.0300.0250.0410.0280.0410.0500.0340.0340.0130.0330.0440.0500.5661.0000.4800.5070.5450.3510.5270.4720.3720.4960.3810.3080.4790.6460.4740.3670.4120.4500.0130.3530.3420.2530.0430.0580.2270.3630.2460.0711.0000.1470.1600.2420.2040.0410.0500.2210.4350.1310.1240.0730.1380.1220.3370.1360.2380.2540.1040.0920.0900.1040.1040.1010.0720.0000.5650.106
POLY_0300.1300.0380.0450.0910.0710.0980.2240.0390.0400.0390.0620.0570.0250.0960.0990.0840.1090.1260.0730.2850.2510.0830.1710.2490.0600.1390.3050.0720.1590.1600.2270.0720.1390.0350.0000.2880.0460.0190.2010.1780.0170.1420.1010.1160.0600.0430.0320.0600.1910.0410.0920.4920.1270.2640.2790.0710.1260.2150.2140.1940.1920.0390.0350.0400.0400.0480.0440.0510.0440.0690.0670.0320.0330.0220.0290.0710.0720.4600.4801.0000.4570.4520.3480.4660.4120.3550.4440.3510.2850.3780.4260.3860.3170.3670.4170.0150.2990.2620.2560.0360.0740.2220.2780.2100.0731.0000.1650.2480.2280.1830.0420.0620.2590.2730.1480.1320.1070.1630.1220.2500.1760.2790.2470.1480.1140.1280.1380.1340.1360.1120.0000.3030.086
POLY_0400.0380.0060.0520.0580.0370.0470.3350.0450.0240.0250.0370.0520.0280.1060.0730.0520.1120.1600.0830.4500.0720.0230.0590.2060.0170.0520.2690.0580.0880.1240.1150.0420.0500.0030.0080.0780.0200.0000.0610.0580.0140.0390.0560.0650.0180.0160.0100.0120.2290.0000.0960.1600.0530.5080.3150.0690.0450.0840.1120.0980.0640.0260.0370.0290.0400.0310.0200.0430.0290.0220.0370.0360.0410.0100.0370.0260.0380.6130.5070.4571.0000.5110.3730.5160.4570.3930.5090.4080.3580.5410.4670.4770.3490.4260.4690.0050.2480.2890.1760.0340.0540.2020.3440.1400.0591.0000.1060.0620.1560.1090.0260.0410.1680.4510.1110.0970.0540.1100.1070.3500.0650.1550.1550.0460.0800.0580.0460.0720.0760.0400.0000.2400.066
POLY_0500.0780.0230.0500.0660.0650.0750.2410.0390.0240.0230.0440.0480.0260.0900.0770.0620.1080.1340.0670.3360.1530.0530.1090.1970.0430.1080.5860.0930.1220.1410.1770.0690.1000.0210.0080.2180.0280.0120.1970.1540.0120.0990.0720.0850.0410.0300.0270.0370.2090.0280.1020.2390.0780.3000.3570.0750.0900.1430.1710.1520.1030.0320.0380.0350.0420.0350.0310.0410.0320.0470.0500.0350.0370.0230.0340.0490.0510.5280.5450.4520.5111.0000.3690.4840.4340.3630.4660.3790.2860.4360.4540.5850.3300.3860.3850.0100.2830.3000.2280.0340.0620.2000.2860.2110.0681.0000.1470.1700.2150.1980.0340.0480.2020.3310.1330.1180.0810.1440.1220.2770.1370.2320.2340.1050.0920.1120.1060.1090.1090.0740.0000.3560.077
POLY_0600.0990.0260.0280.0510.0550.0600.1930.0510.0360.0270.0450.0480.0160.1010.0870.0740.1030.1270.0580.2140.1560.0450.1030.1620.0380.0920.2290.0490.1320.1260.1460.0540.0840.0200.0000.1850.0360.0160.1280.1150.0170.0960.0730.0870.0350.0270.0310.0350.1530.0210.0990.2070.0660.2090.2710.0490.0940.1680.1640.1420.1440.0230.0240.0260.0370.0330.0310.0380.0360.0390.0450.0450.0530.0140.0290.0400.0480.3740.3510.3480.3730.3691.0000.4230.3720.3280.3820.2970.2550.3190.3280.3010.5620.3350.3350.0200.2400.2010.1970.0370.0790.1800.2170.2020.0931.0000.1540.1570.2340.3620.0660.1550.1850.2200.1150.0870.0860.1120.0890.2170.1130.2080.1920.1130.0960.0960.1050.0980.1010.0770.0000.2340.059
POLY_0700.0780.0220.0470.0700.0630.0750.2370.0520.0390.0370.0620.0620.0350.1090.0770.0840.1170.1490.0730.3590.1460.0520.1050.2060.0360.0930.3020.0640.1600.1440.1730.0640.0970.0230.0180.2190.0560.0000.1800.1440.0080.0910.0720.1000.0370.0290.0260.0470.2220.0140.1280.2460.0820.3040.2930.0700.0790.1440.1750.1490.1320.0230.0290.0330.0400.0340.0290.0370.0310.0390.0440.0290.0340.0110.0280.0410.0470.4620.5270.4660.5160.4840.4231.0000.5150.4010.5230.4340.3540.4360.4730.4450.3440.4640.5830.0000.3350.3180.2800.0420.0820.2470.3400.3620.1161.0000.1710.1520.2900.1790.0440.0590.2450.3340.1230.1060.0770.1350.1260.2970.1280.2680.2390.0970.0880.0950.0990.0960.0960.0740.0000.3320.078
POLY_0800.0810.0220.0460.0640.0620.0700.2800.0620.0440.0400.0560.0540.0330.1070.1000.0800.1330.1500.0750.3440.1450.0440.0970.1850.0380.0900.2760.0600.1810.1480.1590.0630.0860.0100.0000.1830.0320.0000.1360.1150.0010.0850.0720.0960.0380.0310.0150.0350.2170.0200.1250.2190.0710.3280.2670.0600.0890.1450.1730.1380.1290.0270.0350.0320.0400.0300.0250.0450.0340.0330.0440.0340.0410.0120.0290.0340.0400.4300.4720.4120.4570.4340.3720.5151.0000.3980.4820.4280.3070.4100.4690.3930.3410.4050.4360.0110.3700.3570.3800.0490.1150.2610.3140.2360.0941.0000.1910.1460.2590.1820.0410.0570.2030.3320.1240.1010.0800.1190.1070.2850.1130.2450.2250.0930.0910.0990.0890.0960.0960.0690.0000.2910.072
POLY_0900.0950.0230.0510.0560.0550.0650.2010.0470.0730.0600.0800.0740.0320.0810.1060.0840.1200.1110.0670.2600.1310.0370.0910.1500.0310.0800.2020.0450.2330.1120.1160.0600.0770.0080.0240.1280.0370.0000.0790.0870.0000.0750.0650.0850.0300.0250.0300.0470.1990.0050.1920.1680.0640.2190.1780.0530.0980.1570.1570.1290.1230.0300.0350.0260.0380.0340.0360.0450.0400.0350.0390.0380.0500.0190.0320.0350.0420.3570.3720.3550.3930.3630.3280.4010.3981.0000.5040.4490.3700.3020.3280.3080.2860.3330.3510.0050.2220.2310.2200.0380.0920.1830.2180.1870.1121.0000.3420.1360.2620.1380.0420.0560.1720.2600.0790.0810.0780.0990.0920.2240.0960.2120.1930.0910.0820.0900.0820.0810.0840.0680.0000.1900.055
POLY_1000.0810.0240.0400.0550.0470.0560.2510.0440.0480.0490.0710.0560.0310.0910.0810.0850.1130.1240.0700.3230.1290.0440.0910.1750.0350.0750.2630.0580.1980.1290.1430.0610.0830.0070.0180.1670.0540.0000.1170.1050.0140.0750.0700.1010.0360.0320.0310.0540.2530.0110.1440.2020.0740.3080.2680.0710.0910.1590.1870.1410.1250.0280.0370.0320.0430.0330.0270.0430.0370.0340.0410.0330.0420.0160.0310.0350.0430.4840.4960.4440.5090.4660.3820.5230.4820.5041.0000.4940.3670.4150.4330.3910.3520.5370.4570.0080.2980.2980.2430.0430.0760.2160.3060.2480.0971.0000.2180.1340.3910.1630.0470.0620.2120.3130.1090.1050.0830.1180.1030.2730.1070.2540.2290.0870.0800.0880.0820.0840.0840.0640.0000.2630.075
POLY_1100.0670.0190.0330.0430.0380.0460.2100.0390.0490.0440.0620.0640.0310.0910.0830.0770.1090.1180.0630.2640.0970.0310.0710.1540.0250.0610.1990.0470.2910.1000.1110.0520.0690.0160.0060.1320.0250.0050.0860.0820.0000.0630.0560.0750.0250.0240.0220.0320.1840.0020.1700.1630.0650.2400.1960.0550.0770.1310.1490.1070.0970.0230.0300.0240.0330.0260.0280.0350.0330.0300.0370.0350.0400.0150.0270.0290.0340.3510.3810.3510.4080.3790.2970.4340.4280.4490.4941.0000.4040.3160.3620.3240.2990.3600.3610.0080.2490.2330.2160.0410.0820.1880.2410.1830.0821.0000.2060.1070.2390.1270.0440.0550.1590.2750.0960.0820.0610.0910.0870.2290.0850.2010.1750.0710.0680.0670.0620.0740.0750.0560.0550.2060.061
POLY_1200.0750.0130.0350.0410.0440.0490.1760.0660.0610.0590.0760.0730.0260.0690.0680.0600.0930.1010.0470.2350.1010.0280.0650.1310.0170.0590.1540.0390.1900.0870.0900.0470.0560.0160.0280.1010.0180.0150.0590.0620.0170.0610.0640.0630.0200.0190.0210.0310.1650.0060.3910.1170.0400.2120.1600.0510.0830.1230.1270.1250.0940.0220.0250.0200.0240.0260.0200.0320.0290.0230.0290.0370.0450.0120.0250.0160.0270.3140.3080.2850.3580.2860.2550.3540.3070.3700.3670.4041.0000.2810.3080.2720.2380.2900.3150.0140.1640.1980.1550.0330.0960.1440.1870.1330.0791.0000.1920.0940.1730.1040.0340.0710.1480.2350.0600.0560.0610.0570.0720.1740.0740.1380.1230.0700.0690.0650.0670.0680.0670.0560.0000.1700.052
POLY_1300.0470.0100.0510.0570.0530.0610.3220.0390.0150.0150.0290.0400.0290.1120.0850.0620.1250.1500.0780.4730.0980.0300.0730.2260.0230.0620.3550.0710.1080.1280.1510.0520.0670.0080.0120.1400.0270.0000.1350.1080.0000.0640.0640.0910.0250.0210.0180.0200.2310.0120.1000.2010.0620.4480.4750.0790.0570.0920.1430.1100.0850.0350.0450.0340.0440.0340.0230.0450.0300.0320.0440.0440.0480.0160.0370.0300.0420.6540.4790.3780.5410.4360.3190.4360.4100.3020.4150.3160.2811.0000.6370.6270.4620.5450.6010.0040.2930.3490.2180.0390.0600.2020.3710.2080.0651.0000.1330.0990.1910.1730.0340.0480.2010.4500.1280.1100.0630.1440.1130.3500.0980.1940.2110.0710.0890.0810.0750.0770.0790.0580.0000.3130.072
POLY_1400.0600.0180.0600.0650.0650.0690.3270.0590.0240.0140.0450.0550.0350.1320.0910.0730.1490.1800.0880.4630.1220.0440.0890.2310.0340.0780.3210.0750.1380.1320.1600.0560.0840.0100.0120.1840.0240.0070.1670.1260.0000.0770.0790.1020.0330.0290.0210.0280.2390.0170.1100.2260.0750.4080.3660.0810.0600.1160.1420.1250.1010.0260.0400.0300.0460.0290.0180.0420.0290.0330.0470.0340.0370.0150.0330.0340.0420.5030.6460.4260.4670.4540.3280.4730.4690.3280.4330.3620.3080.6371.0000.6680.5120.5930.6160.0130.3420.3710.2570.0400.0760.2260.4030.2470.0801.0000.1510.1220.2160.1800.0380.0510.2090.4560.1310.1330.0680.1400.1330.3720.1220.2370.2150.0840.0940.0870.0820.0880.0850.0620.0000.4690.091
POLY_1500.0580.0130.0670.0610.0600.0630.2940.0590.0310.0270.0510.0580.0340.1060.0750.0540.1190.1580.0770.4220.1130.0320.0870.2240.0250.0830.4550.0770.1140.1470.1700.0570.0810.0090.0070.1630.0280.0100.1720.1320.0110.0710.0710.0840.0260.0210.0260.0230.2300.0170.0990.2110.0630.4190.3690.0760.0670.1090.1550.1250.0880.0300.0460.0290.0450.0320.0220.0430.0260.0340.0460.0350.0410.0210.0350.0320.0410.5300.4740.3860.4770.5850.3010.4450.3930.3080.3910.3240.2720.6270.6681.0000.4640.5480.5580.0130.2940.3360.2270.0360.0690.2100.3780.2220.0741.0000.1410.1210.2050.1650.0310.0520.2060.4150.1160.1160.0680.1420.1280.3420.1070.2290.2270.0720.0900.0920.0820.0820.0820.0580.0000.3330.075
POLY_1600.0650.0170.0360.0460.0460.0530.2190.0510.0420.0250.0500.0650.0280.1010.0860.0760.1070.1430.0630.2820.1120.0310.0780.1510.0270.0710.2240.0500.1370.1190.1410.0530.0690.0110.0050.1690.0300.0060.1370.1100.0000.0680.0620.0710.0230.0200.0170.0230.1810.0160.0930.1770.0580.2570.2480.0540.0740.1220.1390.1190.1020.0240.0310.0240.0400.0270.0210.0400.0310.0290.0400.0470.0520.0080.0270.0300.0390.3840.3670.3170.3490.3300.5620.3440.3410.2860.3520.2990.2380.4620.5120.4641.0000.5030.4910.0140.2660.2620.2160.0330.0820.1920.3110.2200.0891.0000.1430.1210.2270.2920.0540.1250.1860.2750.1070.0970.0760.1050.1080.2540.0950.1990.1960.0770.0830.0830.0730.0750.0740.0550.0000.2490.061
POLY_1700.0660.0170.0460.0520.0520.0560.2640.0470.0400.0310.0440.0510.0310.1110.0900.0810.1270.1520.0750.3470.1130.0310.0830.1990.0260.0700.2830.0560.1550.1330.1420.0570.0720.0070.0060.1800.0450.0040.1220.1050.0060.0700.0660.0870.0260.0230.0230.0410.2360.0060.1190.1820.0620.3390.2940.0640.0720.1340.1640.1360.1080.0230.0320.0290.0410.0270.0240.0360.0290.0310.0350.0320.0360.0150.0290.0270.0340.4250.4120.3670.4260.3860.3350.4640.4050.3330.5370.3600.2900.5450.5930.5480.5031.0000.6460.0010.2980.3090.2440.0420.0850.2320.3690.2460.0941.0000.1730.1180.3340.1690.0400.0560.2170.3330.1080.1060.0770.1220.1220.3020.1030.2120.2110.0750.0830.0870.0740.0790.0800.0600.0000.2670.066
POLY_1800.0650.0160.0430.0580.0560.0670.2770.0660.0440.0390.0620.0690.0360.1250.0890.0910.1200.1480.0800.4120.1240.0390.0910.2250.0290.0760.3280.0640.1570.1290.1530.0590.0810.0080.0100.1820.0430.0050.1570.1230.0000.0780.0660.0960.0280.0240.0240.0400.2600.0130.1180.2290.0720.3650.3060.0720.0720.1300.1590.1390.1110.0230.0330.0290.0450.0290.0230.0380.0310.0320.0430.0370.0380.0080.0290.0310.0400.4670.4500.4170.4690.3850.3350.5830.4360.3510.4570.3610.3150.6010.6160.5580.4910.6461.0000.0080.3650.3420.2700.0430.0790.2290.3920.3380.0961.0000.1660.1220.2760.1900.0420.0530.2310.3800.1240.1160.0710.1260.1310.3420.1160.2510.2410.0790.0840.0810.0830.0760.0810.0640.0000.3410.078
PROVID0.0690.0350.0430.0290.0740.0400.0070.0100.0710.0960.0680.0420.0430.0100.0070.0370.0000.0000.0150.0030.0730.0670.041-0.0220.049-0.0590.0100.0330.0160.0180.0360.0450.0520.1990.0460.0480.0430.0850.0260.0260.2600.0460.0340.0420.0610.046-0.0000.0070.0170.0480.0170.0390.0350.0100.0060.0390.0700.0290.0250.0240.0470.0600.0350.0470.0400.0480.0300.0280.0260.0970.0880.0360.0370.0460.0310.1130.0920.0100.0130.0150.0050.0100.0200.0000.0110.0050.0080.0080.0140.0040.0130.0130.0140.0010.0081.0000.0000.0000.0100.0510.0300.0110.0000.0090.0240.0020.0220.0480.0180.0490.0520.0610.0010.0130.0130.0190.0200.0180.0130.0000.0390.0060.0210.051-0.064-0.068-0.065-0.062-0.065-0.0460.5700.0080.029
PR_0300.0830.0340.0430.0670.0590.0690.2110.0370.0360.0260.0480.0410.0320.0930.0830.0720.0960.1200.0840.2920.1560.0740.1250.2190.0560.1310.3050.0910.1740.1210.1580.0710.1090.0270.0000.2020.0430.0060.1440.1750.0070.1230.0890.1090.0550.0430.0400.0690.2010.0310.1050.2310.1050.2460.2880.0950.0850.1390.1580.1360.1240.0390.0400.0440.0520.0400.0430.0500.0540.0610.0740.0430.0450.0200.0320.0570.0680.2840.3530.2990.2480.2830.2400.3350.3700.2220.2980.2490.1640.2930.3420.2940.2660.2980.3650.0001.0000.3820.3920.0600.0830.3640.2960.3060.0831.0000.1820.1500.2610.2060.0550.0580.2030.2600.1350.1510.1010.1630.1460.2640.1430.2440.2490.1190.1180.1330.1230.1310.1270.1020.0000.3490.106
PR_0500.0640.0220.0440.0620.0560.0640.2550.0390.0340.0280.0480.0510.0420.1220.0850.0640.1170.1340.0940.3430.1010.0510.0980.1910.0410.0850.2610.0810.1260.1100.1210.0610.0730.0060.0000.1290.0230.0000.1090.1280.0000.0940.0860.1070.0450.0390.0260.0370.1870.0150.1210.1960.0830.2920.2840.0890.0670.1030.1260.0960.0980.0420.0510.0440.0620.0410.0340.0540.0530.0430.0580.0520.0570.0180.0480.0400.0530.3510.3420.2620.2890.3000.2010.3180.3570.2310.2980.2330.1980.3490.3710.3360.2620.3090.3420.0000.3821.0000.3230.0600.0760.2510.3210.1810.0631.0000.1580.0940.1940.1290.0550.0410.1680.2750.1370.1190.0870.1470.1220.2520.0950.1960.2010.0840.0950.0950.1100.0900.0960.0730.0000.2660.095
PR_0600.1160.0400.0340.0740.0630.0810.1500.0360.0490.0490.0560.0680.0380.0830.0920.0860.0940.1090.0740.1850.1780.0770.1460.1900.0610.1470.2410.0910.2190.1160.1480.0720.1190.0280.0100.1940.0450.0090.1370.1790.0000.1410.1140.1310.0600.0480.0430.0750.1670.0380.1360.2270.1080.1740.2050.0830.1090.1640.1660.1520.1500.0450.0390.0460.0570.0470.0430.0560.0560.0680.0760.0490.0520.0150.0320.0600.0720.2260.2530.2560.1760.2280.1970.2800.3800.2200.2430.2160.1550.2180.2570.2270.2160.2440.2700.0100.3920.3231.0000.0800.1500.4140.2070.2470.0831.0000.2170.1820.2660.1940.0680.0540.1990.1590.1510.1400.1170.1610.1370.1870.1540.2520.2340.1340.1310.1420.1460.1420.1420.1180.0360.2480.102
PR_0900.1520.3770.0400.0380.0480.0370.0500.0360.0890.1040.0820.0560.0560.0490.0750.1000.0640.0420.0490.0510.0960.4120.0530.0520.4030.0580.0550.5230.0670.0500.0390.0930.0610.0470.0670.0310.0500.0520.0230.0340.0240.0550.0450.0430.3900.3910.0420.0730.0520.0580.0480.0500.5040.0470.0510.5180.0960.0780.0610.0620.1110.1470.1500.1510.1510.1720.1730.1710.1730.1730.1730.1880.1790.1570.1590.1750.1730.0350.0430.0360.0340.0340.0370.0420.0490.0380.0430.0410.0330.0390.0400.0360.0330.0420.0430.0510.0600.0600.0801.0000.0780.0620.0520.0440.0761.0000.0690.0560.0750.0460.6400.0500.0520.0480.0280.0250.0350.0300.0290.0520.0640.0710.0590.0880.0460.0600.0430.0430.0460.0560.0090.0540.513
PR_1000.1370.0600.0280.0380.0500.0450.0390.0550.0860.0820.0860.0660.0560.0550.0720.0780.0530.0420.0500.0470.1030.0710.0700.0800.0530.0940.0450.0580.0910.0550.0530.0710.0700.0180.0620.0610.0260.0130.0450.0740.0000.0940.0740.0690.0670.0600.0360.0770.0600.0650.0840.0620.0680.0540.0430.0600.1160.1200.1020.0930.1180.0360.0410.0330.0410.0440.0470.0450.0500.0440.0460.0400.0420.0350.0410.0410.0450.0510.0580.0740.0540.0620.0790.0820.1150.0920.0760.0820.0960.0600.0760.0690.0820.0850.0790.0300.0830.0760.1500.0781.0000.1290.0560.0540.2711.0000.1010.0980.0890.0460.0620.2190.0680.0460.0620.0610.0720.0550.0510.0430.0820.0690.0600.0990.0930.0980.0930.0930.0910.0950.0000.0490.063
PR_1100.1490.0400.0430.0740.0660.0800.1900.0690.0710.0660.0920.0880.0330.0990.1040.1140.1230.1320.0670.2360.2390.0750.1600.2620.0590.2200.2530.0790.2230.1520.1890.0850.1220.0120.0330.2460.0880.0100.160-0.2470.0000.1310.1040.1140.0580.0480.0620.1020.2120.0650.1510.2670.1000.2080.2280.0800.1400.2150.2170.1810.1870.0420.0390.0400.0500.0450.0420.0490.0510.0610.0660.0370.0470.0190.0290.0560.0640.2220.2270.2220.2020.2000.1800.2470.2610.1830.2160.1880.1440.2020.2260.2100.1920.2320.2290.0110.3640.2510.4140.0620.1291.0000.2260.3050.0960.0070.2550.2420.3290.2160.0550.0720.2260.1970.1350.1190.1160.1470.1320.2100.1670.3140.2850.1490.2260.2180.2310.2220.2200.1410.0110.2780.095
SAL_0100.0520.0150.0560.0630.0480.0640.5020.0750.0350.0240.0480.0710.0530.1400.0930.0710.1350.1760.1130.6200.1050.0420.0880.2020.0320.0910.4430.0890.1150.1250.1440.0560.0710.0230.0050.1340.0270.0000.1200.1400.0140.0780.0920.0900.0320.0310.0240.0350.3750.0100.0970.3010.0890.5750.4970.1020.0600.1140.1140.1100.0910.0270.0400.0420.0490.0330.0310.0460.0390.0440.0610.0400.0500.0180.0370.0450.0610.3510.3630.2780.3440.2860.2170.3400.3140.2180.3060.2410.1870.3710.4030.3780.3110.3690.3920.0000.2960.3210.2070.0520.0560.2261.0000.1830.0631.0000.1280.0860.1990.1170.0470.0460.3250.5910.1330.1220.0710.1440.1300.5510.1010.1890.2150.0750.1020.1040.0750.0960.0830.0680.0000.4440.096
SED_0300.0920.0360.0310.0640.0590.0710.1170.0300.0410.0340.0470.0440.0330.0720.0700.0700.0640.0840.0640.1410.1540.0760.1290.1660.0580.1300.2010.0670.1560.1030.1350.0670.1080.0280.0030.1700.0650.0080.1510.1740.0120.1220.0850.0970.0580.0480.0370.0780.1360.0310.1030.2070.1000.1210.1870.0720.0790.1240.1330.1120.1260.0320.0290.0410.0510.0430.0360.0420.0440.0560.0590.0380.0300.0180.0220.0550.0590.1820.2460.2100.1400.2110.2020.3620.2360.1870.2480.1830.1330.2080.2470.2220.2200.2460.3380.0090.3060.1810.2470.0440.0540.3050.1831.0000.0981.0000.1600.1550.2480.2890.0600.0530.1690.1170.1250.1120.0930.1330.1250.1440.1310.2510.2230.1190.1100.1310.1240.1130.1150.1000.0000.2660.095
SED_0500.1050.0490.0350.0400.0530.0410.0550.0760.1130.1080.1100.0950.0770.0550.0760.0910.0660.0690.0570.0390.0960.0580.0700.0810.0490.0920.0490.0540.0720.0600.0540.0730.0820.0300.0570.0780.0620.0140.0540.0820.0120.0870.0730.0820.0560.0480.0480.1050.0650.0430.0750.0640.0660.0570.0540.0520.1020.1190.1110.1070.1220.0380.0380.0370.0460.0460.0430.0460.0500.0440.0460.0430.0460.0410.0430.0430.0460.0600.0710.0730.0590.0680.0930.1160.0940.1120.0970.0820.0790.0650.0800.0740.0890.0940.0960.0240.0830.0630.0830.0760.2710.0960.0630.0981.0001.0000.1030.1030.1050.0680.0870.2770.0800.0420.0620.0630.0740.0630.0670.0560.0860.0800.0770.1000.0930.0940.0930.0930.0930.0880.0000.0670.057
SEQID1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000-0.0011.000-0.0071.0001.0001.0001.0001.0001.0001.0000.0111.0001.0001.0001.0001.000-0.0031.0001.0001.0001.0001.0001.0000.0050.0001.000-0.0021.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0021.0001.0001.0001.0001.0000.0071.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000-0.002-0.005-0.005-0.010-0.006-0.0080.0011.0001.000
SLP_0100.1620.0590.0400.0770.0710.0830.1100.0500.0960.0900.1000.0990.0550.0860.1220.1140.1110.0890.0830.1070.1960.0910.1540.1690.0680.1570.1590.0730.4280.1160.1300.1020.1310.0350.0290.1450.0730.0130.1010.1550.0060.1580.1220.1400.0790.0590.0520.1090.1710.0410.3370.1680.1070.1150.1410.0770.1640.1980.1700.1620.1790.0500.0400.0520.0600.0600.0670.0610.0700.0710.0740.0520.0620.0360.0360.0760.0810.1420.1470.1650.1060.1470.1540.1710.1910.3420.2180.2060.1920.1330.1510.1410.1430.1730.1660.0220.1820.1580.2170.0690.1010.2550.1280.1600.1031.0001.0000.1870.3480.1440.0810.0720.1710.1010.1110.1010.1290.1190.1050.1270.1590.2070.1760.1640.1560.1590.1550.1550.1550.1500.0000.1570.085
SS_0100.3520.1640.1000.1770.1950.1910.0660.0250.0680.0600.0900.0630.0500.0630.1070.1130.0880.0540.1070.0660.5650.2980.5200.5170.2570.9360.2040.1430.1510.2510.3250.1920.3550.1400.0000.4370.0750.0430.3171.0000.0390.6280.4440.3850.2650.1830.1140.1850.1350.2350.1250.3400.2310.0780.1320.1140.3130.3470.2850.2630.3420.1730.1100.1350.1220.2080.1800.1750.1820.2270.2060.0430.0420.0960.0630.2110.2050.1240.1600.2480.0620.1700.1570.1520.1460.1360.1340.1070.0940.0990.1220.1210.1210.1180.1220.0480.1500.0940.1820.0560.0980.2420.0860.1550.1031.0000.1871.0000.2090.1690.0800.0880.2780.0570.5110.3800.4030.3290.3000.0900.5580.2610.2540.5610.5420.5760.5820.5550.5510.5640.0000.1980.156
STI_0300.1370.0600.0360.0780.0640.0790.1520.0500.0760.0750.0960.0830.0520.0820.0950.1110.0990.0920.0860.1590.1940.1020.1570.2020.0830.1640.2250.0980.2930.1230.1460.0970.1370.0150.0240.2040.0900.0040.1380.1880.0040.1580.1350.1520.0830.0670.0570.1120.1980.0410.2250.2240.1280.1700.2200.0990.1420.2160.2120.1690.1750.0450.0420.0510.0620.0580.0640.0590.0720.0740.0750.0520.0480.0320.0390.0720.0770.2090.2420.2280.1560.2150.2340.2900.2590.2620.3910.2390.1730.1910.2160.2050.2270.3340.2760.0180.2610.1940.2660.0750.0890.3290.1990.2480.1051.0000.3480.2091.0000.2170.0940.0830.2180.1360.1490.1400.1440.1530.1410.1540.1770.2730.2620.1580.1460.1650.1570.1590.1600.1410.0000.2430.117
STI_0500.1160.0400.0320.0630.0680.0710.0880.0440.0340.0100.0480.0340.0260.0710.0820.0740.0900.0850.0660.0930.1540.0730.1230.1550.0560.1340.1870.0650.1280.0960.1230.0690.0990.0230.0170.1650.0450.0180.1100.1500.0000.1320.1030.1080.0610.0460.0390.0540.1130.0350.0960.1890.0910.0970.2100.0630.1000.1540.1420.1280.1350.0350.0320.0410.0500.0480.0450.0560.0570.0580.0650.0600.0670.0210.0350.0630.0720.1770.2040.1830.1090.1980.3620.1790.1820.1380.1630.1270.1040.1730.1800.1650.2920.1690.1900.0490.2060.1290.1940.0460.0460.2160.1170.2890.0681.0000.1440.1690.2171.0000.0860.1280.1310.0850.1310.1150.1170.1330.1070.0990.1310.2040.2030.1310.1280.1340.1310.1370.1390.1150.0000.1990.077
STI_0700.1660.3830.0300.0430.0520.0430.0470.0380.0910.0990.0760.0560.0530.0510.0870.1050.0700.0470.0530.0380.1180.4190.0710.0620.4060.0670.0590.4860.0730.0600.0630.1020.0750.0530.0590.0540.0700.0670.0400.0450.0320.0650.0550.0550.4000.4020.0420.0840.0550.0600.0610.0710.4890.0400.0590.4830.1160.1070.0870.0790.1350.1490.1510.1530.1530.1710.1700.1730.1720.1720.1700.1890.1820.1560.1600.1730.1710.0340.0410.0420.0260.0340.0660.0440.0410.0420.0470.0440.0340.0340.0380.0310.0540.0400.0420.0520.0550.0550.0680.6400.0620.0550.0470.0600.0871.0000.0810.0800.0940.0861.0000.1900.0640.0360.0420.0300.0450.0370.0330.0480.0730.0820.0740.0960.0550.0670.0560.0550.0570.0630.0150.0630.484
STI_0800.0790.0330.0480.0450.0600.0480.0360.0760.0830.0790.0820.0800.0610.0600.0530.0640.0500.0570.0450.0330.0900.0390.0600.0640.0350.0910.0450.0310.0530.0420.0430.0360.0520.0760.0540.0610.0660.0530.0480.0700.0230.0770.0610.0570.0400.0360.0600.0630.0340.0520.0550.0620.0430.0320.0490.0320.0880.0920.0860.0720.0710.0400.0540.0350.0460.0430.0370.0600.0520.0430.0530.0560.0610.0400.0500.0390.0480.0520.0500.0620.0410.0480.1550.0590.0570.0560.0620.0550.0710.0480.0510.0520.1250.0560.0530.0610.0580.0410.0540.0500.2190.0720.0460.0530.2771.0000.0720.0880.0830.1280.1901.0000.0580.0320.0500.0420.0600.0510.0450.0370.0650.0580.0530.0780.0780.0750.0750.0780.0770.0710.0190.0440.037
SYN_0100.1630.0660.0510.1020.0880.1050.2650.0450.0550.0530.0690.0620.0380.0860.1070.0970.1010.0960.0880.3620.3350.1350.2470.2780.0990.2160.2960.0920.1340.1330.2000.1000.1890.0370.0100.2320.0620.0270.1420.2250.0200.2220.1680.1840.1030.0780.0490.0950.2270.0650.1240.3250.1460.2930.2930.0910.1480.2250.2070.2080.2240.0530.0430.0660.0680.0730.0740.0700.0750.1000.0930.0420.0400.0330.0320.0990.1000.1930.2210.2590.1680.2020.1850.2450.2030.1720.2120.1590.1480.2010.2090.2060.1860.2170.2310.0010.2030.1680.1990.0520.0680.2260.3250.1690.0801.0000.1710.2780.2180.1310.0640.0581.0000.3200.1700.1560.1670.1800.1640.3190.1880.2350.2370.2030.1890.2010.2120.1980.1960.1890.0000.2990.111
TNB_0100.0310.0080.0610.0520.0390.0550.6170.0470.0210.0060.0350.0400.0290.1200.0800.0600.1370.1690.1270.8000.0690.0310.0640.2370.0220.0630.4120.0850.0880.1080.1150.0420.0580.0090.0240.0840.0040.0000.0740.0880.0030.0490.0640.0720.0250.0190.0120.0160.3540.0040.1040.2760.0790.6810.5530.0910.0380.0770.0840.0760.0540.0340.0450.0410.0580.0350.0290.0520.0330.0380.0540.0500.0590.0260.0430.0370.0480.4150.4350.2730.4510.3310.2200.3340.3320.2600.3130.2750.2350.4500.4560.4150.2750.3330.3800.0130.2600.2750.1590.0480.0460.1970.5910.1170.0421.0000.1010.0570.1360.0850.0360.0320.3201.0000.1230.1250.0510.1260.1110.7030.0660.1050.1240.0470.0750.0610.0490.0640.0640.0430.0000.4280.092
TP_0160.2490.0680.0460.0840.0890.0900.1350.0380.0190.0090.0360.0490.0170.1170.0960.0830.1190.1170.0680.1360.4040.1150.2350.2490.0990.2600.1820.0770.1040.2080.2380.0990.1610.0430.0610.4370.0110.0320.2810.3240.0180.2380.1890.1860.1040.0810.0320.0440.1110.1000.0860.2100.1040.1630.1680.0650.2170.2900.2740.2440.2790.0560.0400.0530.0540.0700.0550.0620.0570.0810.0750.0290.0280.0380.0420.0780.0770.1270.1310.1480.1110.1330.1150.1230.1240.0790.1090.0960.0600.1280.1310.1160.1070.1080.1240.0130.1350.1370.1510.0280.0620.1350.1330.1250.0621.0000.1110.5110.1490.1310.0420.0500.1700.1231.0000.3790.1920.2900.2530.1310.2750.1670.1690.2460.2200.2100.2230.2210.2200.2030.0000.1680.079
TP_0460.1750.0470.0350.0600.0630.0640.1170.0330.0170.0110.0400.0480.0160.0820.0700.0640.0750.0940.0620.1250.2970.0860.1700.2130.0710.1940.1680.0620.0900.1610.2000.0730.1340.0300.0500.3440.0040.0180.2420.2550.0260.1770.1500.1440.0740.0550.0280.0370.1130.0650.0750.1900.0800.1660.1450.0530.1660.2250.2300.1960.2050.0470.0380.0430.0470.0570.0440.0520.0480.0640.0640.0280.0330.0260.0410.0640.0670.1080.1240.1320.0970.1180.0870.1060.1010.0810.1050.0820.0560.1100.1330.1160.0970.1060.1160.0190.1510.1190.1400.0250.0610.1190.1220.1120.0631.0000.1010.3800.1400.1150.0300.0420.1560.1250.3791.0000.1990.2860.2710.1310.2130.1550.1600.1800.1800.1650.1670.1680.1670.1500.0000.1600.061
TP_0560.2750.0710.0270.0740.0940.0840.0610.0350.0730.0710.0890.0580.0310.0630.1060.1200.0850.0630.0610.0570.4090.1090.2200.2050.0820.2510.1250.0640.0970.1520.1690.1010.1600.0390.0660.2450.0440.0410.1430.2140.0030.3850.2060.2100.1090.0780.0370.0780.0880.0710.1040.1800.0870.0760.0930.0540.2470.2950.2420.2390.3130.0560.0270.0530.0450.0890.0850.0720.0900.0760.0630.0230.0220.0360.0310.0690.0640.0670.0730.1070.0540.0810.0860.0770.0800.0780.0830.0610.0610.0630.0680.0680.0760.0770.0710.0200.1010.0870.1170.0350.0720.1160.0710.0930.0741.0000.1290.4030.1440.1170.0450.0600.1670.0510.1920.1991.0000.1840.1590.0760.2120.1720.1530.3080.2520.2610.2820.2630.2640.2590.0000.1140.064
TP_0660.1420.0370.0440.0750.0790.0860.1390.0300.0300.0270.0490.0470.0140.0970.0940.0730.0890.0890.0630.1350.2780.0830.1660.2100.0650.1730.1950.0630.1010.1220.1870.0700.1300.0380.0000.3120.0340.0080.2220.2290.0180.1660.1570.1460.0670.0510.0310.0510.1040.0520.0710.2250.0840.1640.1770.0560.1440.2070.2090.2020.1900.0380.0350.0500.0510.0500.0420.0510.0500.0550.0540.0320.0300.0160.0280.0560.0620.1320.1380.1630.1100.1440.1120.1350.1190.0990.1180.0910.0570.1440.1400.1420.1050.1220.1260.0180.1630.1470.1610.0300.0550.1470.1440.1330.0631.0000.1190.3290.1530.1330.0370.0510.1800.1260.2900.2860.1841.0000.2910.1500.1920.1850.1990.1730.1540.1620.1570.1600.1570.1390.0000.1810.065
TP_0860.1300.0310.0400.0600.0630.0680.1180.0320.0410.0340.0490.0640.0220.0760.0810.0750.0750.0690.0430.1020.2450.0650.1490.1880.0540.1570.1590.0530.0860.1280.1550.0720.1080.0290.0090.3040.0460.0000.2240.2170.0070.1530.1420.1690.0550.0400.0240.0450.1230.0420.0770.1610.0660.1320.1490.0510.1250.1940.1970.2030.1820.0360.0340.0360.0420.0460.0340.0460.0440.0460.0490.0220.0230.0150.0240.0450.0520.1240.1220.1220.1070.1220.0890.1260.1070.0920.1030.0870.0720.1130.1330.1280.1080.1220.1310.0130.1460.1220.1370.0290.0510.1320.1300.1250.0671.0000.1050.3000.1410.1070.0330.0450.1640.1110.2530.2710.1590.2911.0000.1140.1980.1690.1630.1530.1430.1380.1430.1380.1430.1230.0000.1350.058
TRP_0100.0510.0170.0490.0580.0470.0630.5190.0430.0300.0160.0390.0460.0380.1060.0760.0580.1020.1320.0940.7280.1030.0490.0940.2130.0380.0790.4060.0900.1060.1120.1290.0460.0790.0140.0180.1330.0220.0000.1030.1210.0000.0770.0820.0830.0360.0230.0160.0280.3150.0200.0840.2980.0950.5720.5080.0890.0600.0990.0940.0960.0870.0320.0400.0420.0540.0470.0380.0440.0400.0490.0550.0390.0490.0270.0330.0490.0580.3280.3370.2500.3500.2770.2170.2970.2850.2240.2730.2290.1740.3500.3720.3420.2540.3020.3420.0000.2640.2520.1870.0520.0430.2100.5510.1440.0561.0000.1270.0900.1540.0990.0480.0370.3190.7030.1310.1310.0760.1500.1141.0000.0960.1410.1400.0770.0880.0850.0820.0830.0820.0670.0000.4170.107
TS_0110.3710.1240.0620.1020.1090.1080.0780.0380.0880.0900.0990.0750.0430.0790.1290.1400.1010.0820.0880.0830.4630.1800.2650.2870.1630.3610.1860.1080.1320.2010.2390.1400.2050.0760.0510.7020.1290.0480.4180.4850.0330.3420.2110.2410.1700.1320.0800.1410.1340.1170.1120.2510.1510.0830.1460.0950.3240.3510.3020.2700.3540.1330.1010.1140.1050.1530.1400.1360.1360.1550.1420.0590.0520.0910.0750.1460.1460.1190.1360.1760.0650.1370.1130.1280.1130.0960.1070.0850.0740.0980.1220.1070.0950.1030.1160.0390.1430.0950.1540.0640.0820.1670.1010.1310.0861.0000.1590.5580.1770.1310.0730.0650.1880.0660.2750.2130.2120.1920.1980.0961.0000.2110.2080.4550.2940.3060.3300.3120.3100.3030.0080.1740.113
UND_0100.1540.0660.0430.0910.0820.0990.1380.0580.0770.0750.0920.0790.0550.0910.0990.1110.1030.0950.0790.1280.2710.1170.2060.2480.0940.2030.2670.1110.1760.1550.1870.1190.1700.0390.0260.2310.0850.0210.1670.2310.0000.1980.1550.2040.1030.0760.0500.1100.2300.0480.1250.2970.1510.1550.2210.1020.1430.2270.2220.2000.1930.0570.0570.0600.0690.0740.0680.0740.0760.0870.0860.0520.0400.0350.0360.0860.0880.2180.2380.2790.1550.2320.2080.2680.2450.2120.2540.2010.1380.1940.2370.2290.1990.2120.2510.0060.2440.1960.2520.0710.0690.3140.1890.2510.0801.0000.2070.2610.2730.2040.0820.0580.2350.1050.1670.1550.1720.1850.1690.1410.2111.0000.5140.2040.1870.2130.2020.1940.1980.1770.0000.2640.119
UND_0200.1450.0590.0480.0950.0850.1060.1410.0550.0750.0740.0850.0870.0520.0960.1050.1070.0950.0790.0820.1400.2700.1190.2010.2430.0930.2010.2820.1100.1580.1400.1990.1040.1720.0390.0160.2600.0710.0180.1750.2360.0180.1980.1540.1940.0980.0680.0520.1000.2030.0540.1170.3040.1440.1520.2250.0930.1400.2170.2090.1980.1850.0530.0430.0550.0560.0720.0680.0660.0700.0870.0840.0410.0300.0360.0280.0840.0850.2310.2540.2470.1550.2340.1920.2390.2250.1930.2290.1750.1230.2110.2150.2270.1960.2110.2410.0210.2490.2010.2340.0590.0600.2850.2150.2230.0771.0000.1760.2540.2620.2030.0740.0530.2370.1240.1690.1600.1530.1990.1630.1400.2080.5141.0000.1920.1800.1970.2070.1820.1880.1700.0000.2880.114
VAP_0100.5170.1720.0440.1130.1490.1270.0560.0340.1120.1250.1390.0790.0550.0810.1540.1830.1260.0690.1050.0590.6370.2360.3390.3460.1880.4460.1540.1260.1260.2140.2500.1670.2500.0970.1070.4110.1150.0790.2130.3520.0320.5750.3230.3330.2480.1900.0750.1670.1130.1610.1200.2440.1730.0630.1130.1140.4240.4400.3370.3100.4910.1370.0760.1270.1080.2040.1940.1720.1890.1900.1630.0640.0540.1170.0840.1740.1630.0870.1040.1480.0460.1050.1130.0970.0930.0910.0870.0710.0700.0710.0840.0720.0770.0750.0790.0510.1190.0840.1340.0880.0990.1490.0750.1190.1001.0000.1640.5610.1580.1310.0960.0780.2030.0470.2460.1800.3080.1730.1530.0770.4550.2040.1921.0000.4710.4560.5360.4870.4870.4890.0080.1560.131
VAP_0200.4400.1120.0500.1070.1410.1140.0810.0550.1100.1100.1400.0800.0490.0910.1320.1590.1130.0810.0760.0760.6180.1700.3260.5310.1290.7590.1350.0820.1120.1910.2160.1370.223-0.0780.1070.3310.0790.0630.180-0.5400.0350.4350.2960.2620.1800.1290.1070.2140.1150.2540.1190.2130.1230.1290.1180.0750.3770.4020.3190.2870.4480.0890.0540.0750.0710.1390.1330.1230.1320.1310.1170.0290.0320.0680.0510.1170.1140.0930.0920.1140.0800.0920.0960.0880.0910.0820.0800.0680.0690.0890.0940.0900.0830.0830.084-0.0640.1180.0950.1310.0460.0930.2260.1020.1100.093-0.0020.1560.5420.1460.1280.0550.0780.1890.0750.2200.1800.2520.1540.1430.0880.2940.1870.1800.4711.0000.7810.8660.8820.8750.569-0.0420.1350.091
VAP_0300.5260.1340.0590.1260.1460.1370.0690.0470.1290.1330.1570.0860.0620.0840.1330.1590.1230.0760.0800.0760.6670.1990.3340.5370.1470.9230.1650.0960.1270.1960.2260.1430.239-0.0910.0740.3700.0930.0750.290-0.5580.0490.4900.3430.3050.2100.1570.0980.2380.1220.3220.1190.2280.1390.0820.1170.0880.4160.4060.3250.2960.4680.0920.0560.0880.0810.1510.1420.1280.1410.1470.1320.0420.0460.0770.0550.1340.1310.1020.0900.1280.0580.1120.0960.0950.0990.0900.0880.0670.0650.0810.0870.0920.0830.0870.081-0.0680.1330.0950.1420.0600.0980.2180.1040.1310.094-0.0050.1590.5760.1650.1340.0670.0750.2010.0610.2100.1650.2610.1620.1380.0850.3060.2130.1970.4560.7811.0000.7350.7430.7340.363-0.0320.1580.107
VAP_0400.4160.1060.0510.1130.1490.1270.0620.0400.1060.1080.1460.0810.0490.0790.1360.1630.1120.0670.0770.0560.6290.1730.3380.5430.1310.7080.1740.0890.1120.2080.2590.1370.246-0.0840.0830.4310.0850.0640.280-0.5570.0500.5190.2890.2780.1750.1240.1120.2130.1110.2350.1170.2550.1310.0720.1160.0750.3720.4150.3260.2970.4450.0880.0510.0760.0680.1390.1310.1250.1330.1330.1160.0260.0290.0680.0480.1230.1160.0830.1040.1380.0460.1060.1050.0990.0890.0820.0820.0620.0670.0750.0820.0820.0730.0740.083-0.0650.1230.1100.1460.0430.0930.2310.0750.1240.093-0.0050.1550.5820.1570.1310.0560.0750.2120.0490.2230.1670.2820.1570.1430.0820.3300.2020.2070.5360.8660.7351.0000.8960.8970.634-0.0460.1700.094
VAP_050a0.4180.1100.0580.1150.1620.1300.0710.0640.1120.1160.1380.0860.0480.0800.1260.1590.1130.0760.0770.0700.6230.1750.3360.5270.1320.7180.1540.0940.1150.2060.2420.1360.237-0.0780.0870.3800.0890.0640.229-0.5330.0540.4630.2870.2690.1780.1290.1100.2120.1110.2340.1200.2410.1330.0830.1190.0780.3710.4100.3260.2940.4450.0860.0530.0750.0690.1360.1290.1230.1300.1320.1180.0300.0290.0670.0470.1200.1180.0870.1040.1340.0720.1090.0980.0960.0960.0810.0840.0740.0680.0770.0880.0820.0750.0790.076-0.0620.1310.0900.1420.0430.0930.2220.0960.1130.093-0.0100.1550.5550.1590.1370.0550.0780.1980.0640.2210.1680.2630.1600.1380.0830.3120.1940.1820.4870.8820.7430.8961.0000.9770.606-0.0430.1600.097
VAP_050b0.4150.1090.0610.1150.1640.1280.0650.0620.1080.1100.1350.0790.0460.0830.1280.1590.1090.0630.0760.0660.6190.1740.3330.5270.1320.7100.1580.0940.1200.2140.2450.1360.237-0.0820.0860.3740.0880.0660.227-0.5340.0550.4630.2820.2650.1770.1290.1100.2080.1080.2310.1200.2410.1320.0810.1150.0780.3690.4100.3280.2940.4440.0860.0540.0750.0670.1350.1280.1230.1290.1290.1170.0290.0280.0650.0480.1190.1160.0990.1010.1360.0760.1090.1010.0960.0960.0840.0840.0750.0670.0790.0850.0820.0740.0800.081-0.0650.1270.0960.1420.0460.0910.2200.0830.1150.093-0.0060.1550.5510.1600.1390.0570.0770.1960.0640.2200.1670.2640.1570.1430.0820.3100.1980.1880.4870.8750.7340.8970.9771.0000.611-0.0430.1540.097
VAP_0600.4950.1250.0450.1100.1380.1190.0480.0330.1000.1030.1320.0730.0440.0720.1280.1590.1070.0590.0730.0530.6530.1890.3300.3530.1400.3520.1230.0910.1110.1860.2220.1420.235-0.0540.0820.3310.0810.0640.167-0.3560.0320.4950.3150.2880.2000.1460.0760.1100.1010.0860.1150.2110.1320.0570.0890.0820.4050.4090.3170.2840.4700.0940.0530.0890.0800.1510.1420.1290.1410.1440.1290.0350.0340.0720.0520.1320.1290.0600.0720.1120.0400.0740.0770.0740.0690.0680.0640.0560.0560.0580.0620.0580.0550.0600.064-0.0460.1020.0730.1180.0560.0950.1410.0680.1000.088-0.0080.1500.5640.1410.1150.0630.0710.1890.0430.2030.1500.2590.1390.1230.0670.3030.1770.1700.4890.5690.3630.6340.6060.6111.000-0.0420.1250.099
WTPUMF0.0240.0190.0000.0090.0000.0000.0000.0150.0310.0280.0180.0220.0160.0000.0200.0000.0000.0000.0000.0000.0250.0240.000-0.0060.013-0.0210.0000.0090.0230.0110.0000.0000.0000.1800.0330.0000.0300.0140.0000.0210.0450.0020.0000.0000.0140.0120.006-0.0210.0000.1120.0050.0000.0120.0000.0000.0110.0040.0000.0000.0000.0000.0130.0210.0140.0180.0040.0000.0160.0100.0160.0220.0190.0180.0120.0210.0180.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0000.0000.5700.0000.0000.0360.0090.0000.0110.0000.0000.0000.0010.0000.0000.0000.0000.0150.0190.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.008-0.042-0.032-0.046-0.043-0.043-0.0421.0000.0000.011
XTC_0100.0970.0380.0480.0810.0800.0880.3410.0510.0400.0280.0600.0660.0330.1060.0930.0790.1190.1210.0890.4590.1930.0980.1600.2330.0780.1630.4780.1130.1530.1300.1850.0760.1320.0230.0050.2400.0370.0080.2110.2350.0000.1630.1170.1280.0720.0550.0400.0630.2530.0510.0930.3980.1340.4160.4760.1070.1000.1610.1490.1390.1390.0440.0380.0520.0570.0520.0450.0500.0490.0730.0780.0430.0410.0160.0240.0720.0810.3120.5650.3030.2400.3560.2340.3320.2910.1900.2630.2060.1700.3130.4690.3330.2490.2670.3410.0080.3490.2660.2480.0540.0490.2780.4440.2660.0671.0000.1570.1980.2430.1990.0630.0440.2990.4280.1680.1600.1140.1810.1350.4170.1740.2640.2880.1560.1350.1580.1700.1600.1540.1250.0001.0000.152
XTC_0300.2120.3300.0250.0600.0510.0610.0750.0440.0620.0650.0720.0330.0420.0660.0720.0740.0780.0950.0490.1010.2010.4350.1190.1120.3700.1080.1150.6570.0830.0960.1150.0940.1080.0470.0560.1250.0470.0540.0910.0940.0340.1040.0840.0810.3810.3750.0380.0770.0840.0950.0630.1380.7140.0810.1030.7750.1500.1530.1250.1190.1800.1260.1220.1460.1450.1560.1650.1530.1620.1610.1580.1610.1560.1390.1330.1690.1680.0770.1060.0860.0660.0770.0590.0780.0720.0550.0750.0610.0520.0720.0910.0750.0610.0660.0780.0290.1060.0950.1020.5130.0630.0950.0960.0950.0571.0000.0850.1560.1170.0770.4840.0370.1110.0920.0790.0610.0640.0650.0580.1070.1130.1190.1140.1310.0910.1070.0940.0970.0970.0990.0110.1521.000

Missing values

2025-04-17T05:07:07.745375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-17T05:07:08.533365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SEQIDPROVIDGRADEDVGENDERDVURBANDVRESDVORIENTDVDESCRIBEWTPUMFGH_010GH_020SS_010TS_011TP_016TP_046TP_056TP_066TP_086ELC_026aELC_026bELC_026cVAP_010CI_010VAP_020VAP_030VAP_040VAP_050aVAP_050bVAP_060ALC_010NRG_010NRG_020NRG_030NRG_040NRG_050CAN_010CAN_130CAN_140UND_010UND_020MET_010XTC_010HAL_010HER_010COC_010SYN_010BZP_010BS_010TNB_010TRP_010GLU_010SAL_010SLP_010STI_030DEX_010GRV_010STI_080STI_050SED_050SED_030PR_100PR_030PR_050PR_060PR_110POLY_010POLY_020POLY_030POLY_040POLY_050POLY_060POLY_070POLY_080POLY_090POLY_100POLY_110POLY_120POLY_130POLY_140POLY_150POLY_160POLY_170POLY_180PH_010PH_020PH_030PH_040PH_051PH_061PH_052PH_062PH_110PH_120PH_070PH_080PH_130PH_140PH_090PH_100CA_020ELC_041ELC_042ALC_080CAN_050MET_030XTC_030HAL_030COC_030PR_090STI_070DR_010DR_020DR_060DR_070BEH_010BEH_020BEH_030BEH_040BUL_010BUL_020BUL_030BUL_040BUL_050BUL_060BUL_070BUL_080BUL_090BUL_100BUL_110BUL_120DVTY1STDVTY2STDVLAST30
220587107211211.4485782124555555554111111222222221111111111111111111121212111111111111111111111123332323232325545555555555511114444222221222221372
552541271122110.0715043324555555554111111222222221111111111111111111121212111111111111111111111154555555255454543554555555511444444222221222221372
83224359112112147.1653441313455554443512122111222114411112111111111111121212111111111111111111111134343434241212343334433433311334444222211222221362
103714648101122424.6273213224555555554111111222222221111111111111111111121212111111111111111111111124443423223333331111122314211114444222221222221372
11219794691212165.6960681424555555554111111212222221111111111111111111121212111111111111111111111122333433124444443334415511131314444212122212221372
15514204791121511.0928454524555555544112111121222221111111111111111111121212111111111111111111111111111111111111111111111111111113444211221221221372
162663747121121521.0140863524555555554111111222222221111111111111111111121212111111111111111111111123233423132323344444444444411114444222221222221372
1959730109211213.1099033223555555554111111221222221111111111111111111121212111111111111111111111123545555555555555555555555511314444222221222221372
27305702481122626.8279313224554555544312111111222221111111111111111111121212111111111111111111111134343424123454343444322232211314444222221222221372
2855832477121217.0730181224555555554111111222222221111111111111111111121212111111111111111111111144444444444444445555555555511313444211124222221372
SEQIDPROVIDGRADEDVGENDERDVURBANDVRESDVORIENTDVDESCRIBEWTPUMFGH_010GH_020SS_010TS_011TP_016TP_046TP_056TP_066TP_086ELC_026aELC_026bELC_026cVAP_010CI_010VAP_020VAP_030VAP_040VAP_050aVAP_050bVAP_060ALC_010NRG_010NRG_020NRG_030NRG_040NRG_050CAN_010CAN_130CAN_140UND_010UND_020MET_010XTC_010HAL_010HER_010COC_010SYN_010BZP_010BS_010TNB_010TRP_010GLU_010SAL_010SLP_010STI_030DEX_010GRV_010STI_080STI_050SED_050SED_030PR_100PR_030PR_050PR_060PR_110POLY_010POLY_020POLY_030POLY_040POLY_050POLY_060POLY_070POLY_080POLY_090POLY_100POLY_110POLY_120POLY_130POLY_140POLY_150POLY_160POLY_170POLY_180PH_010PH_020PH_030PH_040PH_051PH_061PH_052PH_062PH_110PH_120PH_070PH_080PH_130PH_140PH_090PH_100CA_020ELC_041ELC_042ALC_080CAN_050MET_030XTC_030HAL_030COC_030PR_090STI_070DR_010DR_020DR_060DR_070BEH_010BEH_020BEH_030BEH_040BUL_010BUL_020BUL_030BUL_040BUL_050BUL_060BUL_070BUL_080BUL_090BUL_100BUL_110BUL_120DVTY1STDVTY2STDVLAST30
610681531835121112149.0690493324555555554111111222222221111111111111111111121212111111111111111111111124552312142323343333211112111114444222221222221372
610701955148101112113.5925632213531551411432822512211112422112111111111212221212111111111111111111111124342423242424244444411321111223333221211221221362
6107112587108221211.5370301124555555554111111211122221111111111111111111121212111111111111111111111144444444444444443333333333311114444222221222221372
61073280215972212837.2607722124555555554111111221222221111111111111111111121212111111111111111111111123233313123434334443215513411114444112213222221372
610835379248101112424.7824393224555555554111111221222221111111111111111111121212111111111111111111111124242424242424242324325324411114444221121221121372
6108424454107111211.3696092324555555554111111222222221111111111111111111121212111111111111111111111122555555125555555555555555511114444222221222221372
6108950464891112734.2076084524555555554111111222222221211111111111111111111212111111111111111111111123342433242434443234411114411123344211221222221372
61093593852492112132.1803982224555555554111111122222221111111111111111111121212111111111111111111111134332333233433334344211112211114444222221222221372
61094185323581132373.2321712324555555554111111222222221111111111111111111121212111111111111111111111134343434343434341111111111111114444222221222221372
61095203773571232187.8683302224555555554111111222222221111111111111111111121212111911111111111111111123232323242424344344223222211314444222221222221372